Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling

Tris Eryando; Tiopan Sipahutar; Sandeep Poddar

doi:10.12688/f1000research.135262.1

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Research Article

Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling

[version 1; peer review: 2 approved with reservations]

Tris Eryando ², Tiopan Sipahutar², Sandeep Poddar¹

PUBLISHED 10 Aug 2023

Author details Author details

¹ Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, West Java, 16424, Indonesia
² Research and Innovations, Lincoln University College,, Petaling Jaya, Selangor, 47301, Malaysia

Tris Eryando
Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Visualization, Writing – Review & Editing

Tiopan Sipahutar
Roles: Data Curation, Formal Analysis, Methodology, Visualization, Writing – Original Draft Preparation

Sandeep Poddar
Roles: Visualization, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Emerging Diseases and Outbreaks gateway.

Abstract

Background: This study investigated the determinants of community preventive behavior in complying with the Indonesian regulations to prevent COVID-19 local transmission.
Methods: A cross-sectional study used to collect the data via an online cross using a form created from a google questionnaire forms. A total of 1,802 respondents were gathered at a single point in time. The authors used the Health Belief Model (HBM) approach to measure and create a model of preventive behavior for COVID-19.
Results: The findings showed that self-efficacy and perceived barriers had statistically significant relationships with preventive behavior. However, the goodness of fit index showed that the proposed model was not fit for the data, which means that it was not fit to describe the empirical phenomenon under study.
Conclusions: This study found that more than half of the respondents still had low perceived susceptibility and severity. Only a few respondents had significant barriers to implementing COVID-19 transmission prevention behaviors. Still, most respondents had low perceived self-efficacy, and only 60% had good behaviors related to COVID-19 prevention. We recommended increasing perceived susceptibility and severity by providing the correct information about COVID-19 in the local cultural context.

Keywords

COVID-19; perceive; preventive behavior; Health Belief Model; Indonesia

Corresponding author: Tris Eryando

Competing interests: No competing interests were disclosed.

Grant information: This research was funded by Universitas Indonesia under contract number NKB-630/UN2.RST/HKP.05.00/2022
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2023 Eryando T et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Eryando T, Sipahutar T and Poddar S. Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 1; peer review: 2 approved with reservations]. F1000Research 2023, 12:966 (https://doi.org/10.12688/f1000research.135262.1) First published: 10 Aug 2023, 12:966 (https://doi.org/10.12688/f1000research.135262.1) Latest published: 03 Oct 2024, 12:966 (https://doi.org/10.12688/f1000research.135262.3)

Introduction

The world is currently besieged by the COVID-19 pandemic.¹ As of February 7, 2022, there were 394,381,395 confirmed cases of COVID-19, with the World Health Organization (WHO) reporting 5,735,179 deaths.² The COVID-19 pandemic in Indonesia from mid-2021 until the end of 2021 reached its peak (second wave) when it was dominated by the Delta variant.

COVID-19 prevention regulations largely depend on community compliance and behavior. While changing behavior is a significant challenge in health interventions. An AC Nielsen survey (2020) in six major cities with a total of 2,000 respondents, in collaboration with the United Nations International Children’s Emergency Fund (UNICEF), found that less than one-third (31.5%) of all respondents practiced all three-preventive activities (wearing masks, washing hands, and social distancing). Over one-third (36%) practiced only two of the three-preventive behaviors, less than one-fourth (23.2%) practiced only one of the three behaviors, and almost one-tenth (9.3%) did nothing at all.³ A number of studies described the associations between socio-demographic characteristics and people’s levels of perceptions about the severity of COVID-19 with their preventive behavior.³^–¹⁰

Based on the official government report (see https://covid19.go.id/monitoring-kepatuhan-protokol-kesehatan), some areas of Indonesia showed a compliance rate of only 60% till January 2022.¹¹ The aim of this study was to examine changes in community behaviour to prevent local COVID-19 transmissions and changes in community perceptions about the severity level of COVID-19. The following research questions were formulated: 1) What constitutes as the community perceptions of COVID-19? 2) Is there an association between community perception and community preventive behaviour using the Health Belief Model (HBM) approach? And 3) what kind of model of preventive behaviour on COVID-19 can be predicted using the structural equation model (SEM)?

Methods

Study design and samples

This was a cross-sectional study using Google forms with structured survey questionnaires. The questionnaires have been tested with 30 respondents before the survey was conducted. The proportion of a large population was used to figure out the sample size for a variable, which was used to figure out the minimum sample size.¹² The margin error=5%, p=50%, and Zα=95%. The minimum sample size for this study was 385 respondents. The data collection was conducted over two weeks from July 14–26, 2021, using WhatsApp, Line, and Telegram. To expand the coverage throughout Indonesia, social media influencers were asked to distribute the survey through Twitter and Instagram. The respondents who participated in this study were aged 15–62 and Indonesian citizens. On average, it took 20–25 minutes to complete the form. Respondents signed informed consent forms before completing the survey. The total final number of respondents was 1,802.

Conceptual model

The HBM was employed as a primary reference to measure perception and preventive behavior on COVID-19. The HBM considers several main concepts that predict why people will take action to prevent, including individual characteristics, perceived susceptibility, severity, benefit, and self-efficacy.¹³ This study consisted of 45 observed variables and six latent variables.

Individual characteristics and community perception of COVID-19

Individual characteristics were represented by residence (R), age (A), gender (G), educational level (E), and occupation (O). Perceived susceptibility, severity, benefit, barriers, and self-efficacy are latent variables in this study. All questions in in the questionnaire in each latent variable were answered using a five-point Likert scale: 1 (strongly disagree), 2 (disagree), 3 (neutral), 4 (agree), and 5 (strongly agree). Observed variables X1-14, S1-5, BEN1-5, BAR1-5, and SE1-5 were used to measure perceived susceptibility, severity, benefit, barriers, and self-efficacy, in that order.

Community preventive behavior against COVID-19 transmission

Community preventive behavior was represented by six indicators in Figure 1 which include frequency of hosting (BHV1), frequency of visiting others (BHV2), frequency of work/study from office/school (BHV3), frequency of handshaking (BHV4), frequency travelling to a red zone (BHV5), and frequency of leaving the house when you are not feeling well (BHV6).

Figure 1. Proposed structural model of community preventive behavior against COVID-19.¹⁴

Data analysis

Structural equation modelling (SEM) is a multivariate statistical analysis technique that is used to analyze structural relationships. This technique is the combination of factor analysis and multiple regression analysis, and it is used to analyze the structural relationship between measured variables and latent constructs. The authors used Lisrel version 8.8 software to construct the covariance-based SEM. SEM analysis can also be done using R. Steps of doing SEM analysis using R as follows¹⁵:

a. Draw model
b. Input data in the form of covariance or correlation matrix
c. Identify the model
d. Assess parameter estimates
e. Assess fit measure (chi-square, degree of freedom, residual matrix, GFI, RMSEA)
f. Check the modification indices
g. Rerun the model till we get the best fit of the data to the model ad theory.

The six latent variables were perceived susceptibility, severity, benefit, barriers, self-efficacy, and preventive behavior. The 45 observed variables were presented in Table 1 include construct that build by all the observed variables.

Table 1. The observed variables.

No	Item	Scale	Description	Construct
1	R	1. City 2. Village 3. Housing area 4. Apartment	Residence	-
2	A		Age	-
3	G	1. Male 2. Female	Gender	-
4	E	1. No education 2. Elementary school 3. Junior high school 4. Senior high school 5. University	Education level	-
5	O	1. Unemployment 2. Labourer/employee 3. Student/college 4. Housewife 5. Entrepreneur 6. Pensionary 7. Others	Occupation	-
6	X1	1-5 Likert strongly disagree – strongly agree	I am at risk when not keeping a distance of at least 1-2 metres when in the work area.	Perceive Susceptibility
7	X2		I am at risk when not keeping a distance of at least 1-2 metres when at school.
8	X3		I am at risk of not keeping a distance of at least 1-2 metres when at the mall/supermarket.
9	X4		I am at risk when not keeping a distance of at least 1-2 metres when at the traditional market.
10	X5		I am at risk when not keeping a distance of at least 1-2 metres at a restaurant/coffee shop/café.
11	X6		I am at risk when not keeping a distance of at least 1-2 metres when at a touristdestination.
12	X7		I am at risk when not keeping a distance of at least 1-2 metres when at a wedding reception.
13	X8		I have a low risk of getting COVID-19 if I avoid using a bus/taxi.
14	X9		I have a low risk of getting COVID-19 if I avoid using a commuter line.
15	X10		I have a low risk of getting COVID-19 if I avoid using a small bus.
16	X11		I have a low risk of getting COVID-19 if I avoid using motorcycle taxis/online.
17	X12		I have a low risk of getting COVID-19 if I practice work/study from home.
18	X13		I have a low risk of getting COVID-19 if I practice avoiding handshaking.
19	X14		I have a low risk of getting COVID-19 if I practice avoiding travel to a red zone (an area with a high positive rate).
20	S1		I may get COVID-19 and cause health impacts if I do not keep a distance ofat least 1-2 metresina public area.	Perceive severity
21	S2		I may get COVID-19 and cause health impacts if I do not avoid using public transportation.
22	S3		I may get COVID-19 and cause health impacts if I do not work/study from home.
23	S4		I may get COVID-19 and cause health impacts if I do not avoid handshaking.
24	S5		I may get COVID-19 and cause health impacts if I do not avoid travel to a red zone.
25	BEN1		I feel safe from COVID-19 if I keep a distance of at least 1-2 metresin a public area.	Perceive benefit
26	BEN2		I feel safe from COVID-19 if I avoid using public transportation.
27	BEN3		I feel safe from COVID-19 if I work/study from home.
28	BEN4		I feel safe from COVID-19 if I avoid handshaking.
29	BEN5		I feel safe from COVID-19 if I avoid travel to a red zone.
30	BAR1		I find it difficult to stayat least 1-2 metres from people in a public area.	Perceive barrier
31	BAR2		I find it difficult to avoid using public transportation.
32	BAR3		I find it difficult to work/study from home.
33	BAR4		I find it difficult to avoid handshaking.
34	BAR5		I find it difficult to avoid travel to a red zone.
35	SE1		It is easy for me to avoid travel to a red zone.	Perceive self-efficacy
36	SE2		It is easy for me to avoid handshaking.
37	SE3		It is easy for me to work/study from home.
38	SE4		It is easy for me to avoid using public transportation.
39	SE5		It is easy for me to keep a distance of at least 1-2 metres when in a public area.
40	BHV1		Frequency of hosting.	Behaviour
41	BHV2		Frequency of visiting others.
42	BHV3		Frequency of work/study from office/school.
43	BHV4		Frequency of handshaking.
44	BHV5		Frequency travelling to a red zone.
45	BHV6		Frequency of leaving the house when you are not feeling well.

Descriptive statistics were presented as numbers and percentages for individual characteristics, and bar charts for perceptions and behavior. Descriptive statistical analysis was performed using IBM Statistical Package for the Social Sciences (SPSS) version 27 and presented using Microsoft Excel. The descriptive analysis simply can also be completed using Microsoft Excel. The SEM analysis was carried out using the following steps:

1) Identifying the degree of freedom of the structural model.
2) Assessing construct validity.
3) Assessing construct reliability.
4) Assessing structural model validity.¹⁶^,¹⁷

Perceived was categorized based on the total score. Good/high perceived were those who chose to “strongly agree” on every question. Regarding behavior, good behavior occurs if the respondent states never or rarely. Rarely was included in the category based on the assumption that people might be challenged to practice preventive behavior related to other factors that require them to leave, such as the environment or critical social activities that cannot be abandoned.

Results

The study results are presented in several parts sequentially, starting with the respondent’s characteristics, followed by the descriptive statistics of the independent variable (community perception of the level of severity of COVID-19 disease), the descriptive statistics of the dependent variable (composite variable of community behavior), and the SEM analysis results.

Most of the respondents lived in the city (46.1%) and a housing area (36.1%), and only a few lived in villages (16.6%). A large majority of the respondents were women (74.5%), and approximately 80.0% were students and workers (Table 2).

Table 2. Respondents’ characteristics.

	Frequency	%
Resident
City	831	46.1
Village	299	16.6
Housing area	651	36.1
Apartment	21	1.2
Sex
Male	460	25.5
Female	1342	74.5
Education Level
No education	3	0.2
Elementary school	3	0,2
Junior high school	25	1.4
Senior high school	885	49.1
University	886	49.2
Occupation
Unemployment	128	7.1
Labourer/employee	552	30.6
Student/college	958	53.2
Housewife	35	1.9
Entrepreneur	106	5.9
Pensionary	3	0.2
Others	20	1.1

In terms of perceived susceptibility (Table 3), more than half of respondents (>50%) chose to strongly agree to practice recommended behaviors such as social distancing at least 1-2 meters in public areas; almost half of them strongly agree that they had a low risk of getting COVID-19 when avoiding using public transportation, practicing work/study from home, handshaking, and travelling to a red zone. Most respondents also strongly agree that if they are exposed to COVID-19, it will affect their health if they do not practice recommended behaviors. Likewise, concerning perceived benefit, more than 50% of respondents strongly agree that they can avoid getting COVID-19 if they practice these behaviors. In terms of perceived barriers, less than 20% felt that it would be challenging to implement the recommended behaviors. Regarding self-efficacy, this is defined as the conviction that one can successfully practice a certain behavior,¹³ the survey showed that less than 50% were confident that they could implement the recommended behavior.

Table 3. Distribution (%) of perceived items (N=1802).

Perceive	1 (%)	2 (%)	3 (%)	4 (%)	5 (%)
Perceive Susceptibility
*I am at high risk when I am not keeping a distance of at least 1-2 m when in apublic area*
Working area	3.5	2.1	6.3	27	61.2
School	3.3	2.6	8.0	28	58.1
Mall/supermarket	3.1	1.7	5.7	22.6	67
Traditional market	2.9	1.6	5.5	20.5	69.5
Restaurant/coffee shop/café	2.7	2.6	7.5	27.1	60
Tourism objects	3.4	3.2	10.5	27.1	55.8
Wedding reception	2.9	1.8	6.4	22.1	66.8
*I have a low risk of getting COVID-19 if I avoid using public transportation*
Bus/taxi	6	6	11.4	25.9	50.8
Commuter line	6.4	5.6	12.2	23.9	51.9
City transport	6.4	5.1	9.5	23.5	55.5
Offline motorcycle taxi/online	5.1	10.8	28.4	30.7	25
*I have a low risk of getting COVID-19 if I practice following behaviours*
Work/study from home	3.4	0.9	5.2	19.3	71.2
Avoid handshaking	2.7	0.9	3.4	19	74
Avoid travel to red zone (are with high positive rate)	2.6	1	3.3	16.6	76.5
Perceive Severity
*I may get COVID-19 and cause health impactstomyself if I do notfollow behaviours*
Keep distance at least 1-2 metre when at public area	3.4	1.6	4.9	26.2	63.9
Avoid using public transportation	3.8	2.9	13.5	29.3	50.4
Work/study from home	5.0	2.4	7.3	27.9	57.4
Avoid handshaking	4.3	1.9	4.2	24.5	65.1
Avoid travel to red zone (are with high positive rate)	4.6	1.6	4.2	21.0	68.6
Perceive Benefit
*I feel safe and get off COVID-19 if I practice following behaviours*
Keep distance at least 1-2 metre when at public area	2.6	1.1	4.8	23	68.5
Avoid using public transportation	2.3	1.6	9.3	23.9	62.8
Work/study from home	2.6	1.3	5.9	22.4	67.8
Avoid handshaking	2.6	1.2	4.6	20	71.8
Avoid travel to red zone (are with high positive rate)	2.5	1.2	4.3	18.2	73.9
Perceive of Barrier
*I find it difficult to practice the following behaviours*
Keep distance at least 1-2 metre when at public area	23.3	24.4	16.4	23.6	12.4
Avoid using public transportation	34.8	25.2	15.8	14.1	10
Work/study from home	35.2	25.1	15.9	12.4	11.4
Avoid handshaking	38.8	28.9	14.2	10.2	7.9
Avoid travel to red zone (are with high positive rate)	37.0	25.6	15.9	12.2	9.3
Self-Efficacy
*It is easy for me to practice following behaviour*
Keep distance at least 1-2 metre when at public area	4.5	11.8	17.0	30	36.7
Avoid using public transportation	4.3	9.8	15.5	23.8	46.5
Work/study from home	6.3	8.0	13.0	24.6	48
Avoid handshaking	3.1	5.0	11.2	28.4	52.4
Avoid travel to red zone (are with high positive rate)	5.0	7.2	13.0	23.9	50.9

For each type of recommended behavior as mentioned in Table 1 to prevent the transmission of COVID-19, more than 50% of respondents never and rarely hosted, visited others, worked from the office/school, shook hands, travelled to a red zone, and left the house when not feeling well (Figure 1).

Based on the composite of each perceived item, it was found that only 16% of the respondents had a high percentage of good perceived susceptibility; perceived severity was 43%, perceived benefit was 54%, perceived barrier was 3%, self-efficacy was 19%, and only 60% of respondents practiced good behaviours.

SEM results

Identify degree of freedom of structural model

The second step in SEM analysis is to run the identification of observed variables. A general requirement for identifying any type of model in SEM are the model’s degrees of freedom which must be a least zero (df_M ≥ 0). Hence, the solution to meet the requirement is to identify whether the model is under identified, just identified, or overidentified. Overidentified is mandatory in order to meet the requirement. An overidentified structural equation model is identified and has more observations than free parameters (df_M>0)(2). In this study, we found the degree of freedom value to be 821, hence it was concluded that the model was over-identified. Thus, the next step of the analysis can be completed.

Construct validity

Construct validity was performed to test whether the instrument or measurement variable could describe the latent variable correctly and precisely. For this, two tests were conducted: validity, which consisted of convergent and discriminant validity, and reliability.¹⁴ A convergent validity test examined the loading factor value of the measurement variable in each latent variable construct. If the loading factor value was greater than 0.50, the latent variable construct had good convergent validity.¹⁴ The results of the convergent validity test showed that almost all items in this study had a loading factor value of more than 0.5, except for items X11 and BHV3. These two items have a loading factor of ≤ 0.5, which indicates that they do not meet the criteria for convergent validity. Hair et al. (2019) stated that items with low loading factors that do not meet the limits of convergent validity should be excluded from the measurement of latent variables. Therefore, items X11 and BHV3 in this study were not included in the measurement of the latent variable. The convergent validity test was then carried out for a second time. All of the items had good convergent validity, which was shown by loading factor values of more than 0.5. The discriminant validity test was carried out by comparing the root value of each latent variable’s average variance extracted (AVE) with the correlation of these latent variables with other latent variables. If the root value of the variable AVE was greater than the correlation of the variable with other variables in the model, the indicator/question item had good discriminant validity.

Table 4 shows the AVE root value for each latent variable and the correlation coefficient between the latent variables. The value of the AVE root is shown as the value on the diagonal of the matrix, while the values beside and below the AVE root are the correlation coefficients between two pairs of variables. The results of the evaluation of discriminant validity show that the root value of AVE in each latent variable is greater than the correlation coefficient of the latent variable with other latent variables in the structural model. Thus, it can be stated that the items/instruments in this study have good discriminant validity.

Table 4. Results of the discriminant validity test.

	Suscept	Severity	Benefit	Barrier	Efficacy	Behaviour
Suscept	0.822	-	-	-	-	-
Severity	0.730	0.911	-	-	-	-
Benefit	0.590	0.810	0.936	-	-	-
Barrier	-0.160	-0.210	-0.260	0.737
Efficacy	0.080	0.100	0.120	-0.480	0.725
Behaviour	-0.120	-0.150	-0.160	0.460	-0.320	0.630

Construct reliability

The construct reliability test was done by examining the composite reliability value. If the combined reliability value is greater than 0.7, it can be said that the variables in the study already have reliable indicators/question items.¹⁶ All latent variables were found to have a composite reliability value of more than 0.7, which means that each variable has a consistent measurement indicator and good internal consistency.

Structural model validity

Two analyses were conducted to evaluate the structural model validity:1) dependency test and 2) assessing the goodness-of-fit of the model.

Dependency test

The dependence relationship test was employed by looking at the path coefficient and its p-value in the structural model. The path coefficient shows the magnitude and direction of the relationship between the two variables. According to Table 5, sex had a statistically significant relationship with perceive susceptibility; perceive susceptibility had a statistically significant relationship with perceive severity; age, sex, and education had significant relationships with perceive benefit; perceive benefit, resident, and sex had a significant relationship with perceive barrier; and perceive barrier and occupancy had a significant relationship with perceive self-efficacy. Regarding behavior, only perceived barriers and self-efficacy had statistically significant relationships with COVID-19 prevention behavior. The final structural model is as follows: behaviour = - 0.018*susceptibility - 0.041*severity - 0.00045*benefit + 0.39*barrier - 0.13*self-efficacy.

Table 5. Results of the SEM statistical test.

Latent variable	Independent	Path coefficient	Std. error	t-Statistics	p-value
Perceived Susceptibility	Resident	0.009	-0.010	0.870	0.273
	Age	0.005	-0.005	1.020	0.237
	Sex	0.180	-0.024	7.540	0.000
	Education	0.005	-0.006	0.860	0.276
	Occupancy	0.045	-0.029	1.520	0.126
Perceived Severity	Perceived Susceptibility	0.720	-0.020	35.810	0.000
	Regional	0.012	-0.007	1.690	0.096
	Age	0.001	-0.003	0.280	0.384
	Sex	0.014	-0.017	0.810	0.287
	Education	-0.008	-0.005	-1.680	0.097
	Occupancy	-0.020	-0.021	-0.960	0.252
Perceived Benefit	Perceived Severity	0.810	-0.018	44.660	0.000
	Resident	-0.010	-0.006	-1.550	0.120
	Age	-0.009	-0.003	-3.030	0.004
	Sex	0.034	-0.015	2.270	0.030
	Education	0.008	-0.004	2.040	0.050
	Occupancy	0.032	-0.018	1.770	0.083
Perceived Barrier	Perceived Benefit	-0.240	-0.026	-9.260	0.000
	Resident	-0.028	-0.010	-2.770	0.009
	Age	-0.009	-0.005	-1.940	0.061
	Sex	-0.130	-0.025	-5.050	0.000
	Education	0.009	-0.007	1.340	0.163
	Occupancy	0.051	-0.031	1.670	0.099
Perceived Self-Efficacy	Perceived Barrier	-0.480	-0.031	-15.860	0.000
	Resident	0.008	-0.010	0.840	0.280
	Age	-0.003	-0.005	-0.550	0.343
	Sex	-0.022	-0.024	-0.940	0.256
	Education	0.001	-0.006	0.081	0.398
	Occupancy	0.063	-0.029	2.150	0.040
Individual Behaviour	Perceived Susceptibility	-0.018	-0.037	-0.500	0.352
	Perceived Severity	-0.041	-0.053	-0.780	0.294
	Perceived Benefit	0.000	-0.045	-0.010	0.399
	Perceived Barrier	0.390	-0.036	10.930	0.000
	Perceived Self-Efficacy	-0.130	-0.032	-4.000	0.000

The goodness-of-fit model assessment results in Table 6. showed that none met the goodness-of-fit criteria. Therefore, the structural model in this study was not a fit for the data and was not a fit for describing the empirical phenomenon under study.

Table 6. Goodness-of-fit index results.

Goodness-of-fit index	Cut of value	Value	Conclusion
Probability of χ²	>0.050	0.000	No fit
RMSEA	<0.080	0.140	No fit
GFI	>0.950	0.560	No fit
AGFI	>0.950	0.490	No fit
SRMR	<0.100	0.140	No fit
CFI	>0.950	0.850	No fit
NFI	>0.950	0.850	No fit
PNFI	>0.950	0.770	No fit
PGFI	>0.950	0.480	No fit
IFI	>0.950	0.850	No fit
RFI	>0.950	0.830	No fit

Discussion

The results of grouping all perceived items showed that only a few respondents (16%) had beliefs about the chances of experiencing COVID-19 (perceived susceptibility), but 43% of respondents believed how serious the COVID-19 effects were on their health. More than half of the respondents believed that the recommended behaviors to prevent COVID-19 infection could protect them from getting an infection (perceived benefit). Only 3% of respondents believed some things hindered the practice of recommended behaviors (perceive barrier), and only 19% believed that they could practice the recommended behaviors (perceive self-efficacy). This study’s results are similar to those conducted in India, Sri Lanka, Iran, and Ethiopia.⁸^,¹⁰^,¹⁸^,¹⁹ The HBM theory holds that people are likely to practice preventive behaviors or actions if: 1) They believe that it will reduce their risks,¹³ 2) They believe themselves to be susceptible to COVID-19 infection, 3) They believe that this condition would have a potentially serious impact, 4) They believe that recommended actions/behaviours would be beneficial in reducing their susceptibility to or severity of the virus infection, and 5) They believe that the anticipated benefits of doing preventive behaviours/actions outweigh the barriers.

Perceived susceptibility was not a significant predictor of behavior in this study. This finding is consistent with studies that measured adherence to COVID-19 precautionary measures in China²⁰ and Korea.²¹^,²² A study that used a HBM framework to look at how Saudi Arabian students at Jazan University felt about the COVID-19 vaccine found that perceived susceptibility was not a good predictor of how they felt about getting the vaccine.²³ However, a similar study conducted in Malaysia found that high perceived susceptibility to COVID-19 infection was also associated with the behavior of vaccination intention.²⁴ A study that measured student behavior in the US related to non-pharmaceutical interventions (hand washing with soap and water, use of hand sanitizer, wearing a face mask in public, and practicing social distancing) found that perceived susceptibility was associated with multiple interventions more frequently.²⁵

A previous study conducted by Du Min et al. found that low perceived risk was associated with vaccine hesitancy.²⁶ As with perceived susceptibility, perceived severity was not a significant predictor of preventive behavior in this study. Several studies regarding behavior change using BHM had similar results.²²^,²⁴^,²⁵ Perceived severity, on the other hand, was a significant predictor of preventive behaviors.⁸^,²⁰^–²³^,²⁷ Perceived benefit is one predictor that is not a significant predictor of preventive behavior. This finding is inconsistent with several studies that used BHM to predict behavior change, particularly in relation to COVID-19 prevention.⁸^,²⁰^,²¹^,²³^–²⁷

This study revealed that the perceived barrier was a significant predictor of COVID-19 preventive behaviour. This result is different from a behaviour study conducted in Sri Lanka and Iran, which found that perceived benefit and self-efficacy had a significant positive relationship to COVID-19 prevention behaviour.¹⁰^,²⁸ However, these results were similar to a study conducted in Ethiopia that measured student eating behaviour using the HBM theory in the US²⁹ and other behavioural studies in Iran, India, and Hong Kong.⁸^,¹⁹^,³⁰ This study also found that perceived self-efficacy was a significant predictor of COVID-19 preventive behaviour. The results show that, with lower self-efficacy, people were likely to practice COVID-19 prevention behaviour. This result is similar to several studies that examined behaviours using the BHM theory. Those studies found that perceived self-efficacy has a significant relationship to behaviour.¹⁰^,¹⁸^,¹⁹^,²¹^,²⁸^,³¹ In contrast, a study that assessed the student’s behaviour on the non-pharmaceutical intervention of COVID-19 found that perceived self-efficacy was not a significant predictor of behaviour change. In theory, an individual with good self-efficacy tends to practice recommended action/behaviour,¹³ which is the preventive behaviour of COVID-19. However, this study was unable to confirm these findings.

The findings in this study illustrated that most respondents (97%) had no barriers to practising the recommended behaviour. Still most respondents (81%) were not confident that they could fully implement the recommended prevention behaviours. As many as 60% of respondents practised COVID-19 prevention behaviour well. In knowledge attitude practice (KAP) studies conducted in Indonesia, this finding (percentage of good behaviour) tended to be lower than the other two findings in Indonesia, where the rate of those who performed the correct behaviour for the prevention of COVID-19 was more than 90%.³²^,³³ Studies conducted in other countries also found that respondents who practised COVID-19 prevention behaviours were relatively high (>70%), such as in China, Nepal, Malaysia, Vietnam, and India.³⁴^–³⁸

Fundamentally, perceived susceptibility and severity affect how a person decides to act.¹³ However, most respondents (84%) had low perceived susceptibility in this study. This means that most respondents did not believe that they were also at risk of being affected by COVID-19. This perception represented an obstacle for someone to implement recommended behaviour. It was also known that only 43% believed that if they were infected with COVID-19, they would experience harmful consequences for their bodies. Only half of the respondents believed that the recommended behaviour was able to protect them from COVID-19. This might relate to the information they obtained day-to-day. It was possible that most of them did not have a clear idea about the pathophysiology and epidemiology of COVID-19, made worse by unreliable news or hoaxes circulated on social media, which may have increased negative perceptions about COVID-19.⁸^,³⁹

Regarding obstacles to implementing the recommended behaviour, a few respondents said it was extremely difficult to implement the behaviours (3%). This overall perception then leads to a low belief that respondents are able to implement the recommended behaviour. As a consequence, it will affect COVID-19 prevention behaviour practice. Perception is theoretically influenced by many factors, including demographics and level of knowledge.⁸^,¹⁰^,¹³^,¹⁸^,³²^,³³ A study conducted in China found that knowledge was influenced by educational level and domicile.³⁴ Good knowledge can form a good attitude, which then creates a good perceived.⁴⁰^,⁴¹ COVID-19 was a pandemic that touched almost every facet of human existence. People had to adjust their daily routines to accommodate local government rules in order to reduce the virus transmission, and these behavioural shifts may last long after the disease has passed.⁴² Increasing the respondents’ knowledge is essential to narrowing the gap between knowledge and practice, including myths, hoaxes circulating about COVID-19, and facts. Several important factors that may affect perceived self-efficacy are related to social norms and trust⁴³ in the community. Unfortunately, neither of these factors (including knowledge) was investigated in this study.

The structural model of COVID-19 prevention behaviour in this study was not intended to describe the empirical phenomenon of preventive behaviour. Byrne stated that, if possible, researchers are advised to modify the model by using modification indices (MI) in SEM testing. He said that models with an MI score of more than 10 deserve attention for modification.⁴⁴ Already it has proved that to confront the increase in demand for care, the need for long-term care workforce, and the costs associated with care.⁴⁵ However, Hair et al. suggested that modifying the model should not change the model’s structure significantly.¹⁶

This several studies found a gap in knowledge to inform changes in policies on infection-prevention measures in the community, community infection procedures, the frequency of testing, etc.⁸^,³⁹^,⁴⁶ The unreliable information may increase the negative perception of COVID-19, leading to the community’s obedience to suggested preventive behaviors.³⁹ Moreover, people are more worried about their families and economic conditions due to the spreading pandemic than about complying with lockdown or restriction policies. Several factors that might be related were not examined in this study, such as level of knowledge, social norms, or trust.

Study limitations

Due to the social restrictions (the outdoor community activities are limited by the government based on the law announced every two weeks except for emergency purposes) in Indonesia, the data collection was conducted using a Google form without using a selected sampling method. Thus, the total number of respondents in this study was not representative of the total population in Indonesia and the entire territory of Indonesia. In addition, the entire population of Indonesia did not have the same opportunity to be selected as respondents due to limitations related to internet coverage and utilities. Since the prevalence and impact of COVID-19 are changing dramatically, across-sectional studies were not the best method for describing the changes. Better results might be possible using a longitudinal study.

Conclusions

This study found that more than half of the respondents still had low perceived susceptibility and severity, but more than half had high perceived benefits. Only a few respondents had significant barriers to implementing COVID-19 transmission prevention behaviors. Still, most respondents had low perceived self-efficacy, and only 60% had good behaviors related to COVID-19 prevention.

The results of this study can assist policymakers in developing health interventions related to the preventive behavior of COVID-19 transmission in the community. We recommend increasing perceived susceptibility and severity by providing the correct information about COVID-19 in the local cultural context. The results of this study can also be used as a reference for evidence-based policies so that the government can prioritize the types of interventions to improve public understanding and compliance to prevent the transmission of COVID-19.

The findings of similar studies conducted in other countries showed some consistency. By improving perceived susceptibility and severity, there would be an increase in respondents’ knowledge, increasing perceived susceptibility and severity. The results of this study can, not only be used for COVID-19 prevention policies but can also be used as a reference in making prevention policies against many types of diseases that require community behavior changes. Therefore, when making policies, there are stages that must first be met so that behavior can change, as described in the results of this study.

Ethics approval and consent to participate

The Commission approved this study for Research Ethics and Public Health Service, Faculty of Public Health, University of Indonesia Number: Ket-436/UN2.F10. D11/PPM.00.02/2021.

Consent for publication

Informed consent was obtained from all subjects involved in the study.

Author contributions

All authors made substantial contributions to this research and approved the final manuscript. TE and TS contributed to every step of the study (research concept, design, data interpretation, writing, and review). SP contributed to the research review.

Data availability

Dyrad. Data for: Community perception and COVID-19 prevention, https://doi.org/10.5061/dryad.pnvx0k6rp.⁴⁷

This project contains the following underlying data:

• DATA_FINAL_tio-edit2.csv (Data include all variables in the questionnaire. The data contain 1802 respondents and 89 variables. The variable names of questions 1–23 were given according to the keyword in each question, while the variable names for question number 24–35 were specified according to the question’s number and its answer option. It is recommended to read the variable code definition in sheet 2 and the area code in sheet 3.)

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Figshare. IMPLEMENTATION OF SOCIAL POLICY DISTANCING IN EFFORT PREVENTION COVID-19 IN INDONESIA. https://protect-us.mimecast.com/s/qsa7CkRwomfYPpGvjC2JN1U?domain=doi.org ¹⁴

This project contains the following supplementary material:

• Kuesioner Penelitian.pdf (the google questionnaire used in this study), https://doi.org/10.6084/m9.figshare.23292686.v2.⁴⁸

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgments

The authors are grateful to Prof. Dr. Dewi Susanna, dra, MS., who facilitated and supported the research process, and Universitas Indonesia for financial support. We also thankful to all respondents who were willing to involve in this study by signing the informed consent prior to answer all the questions.

References

1. Bhattacharya S, Basu P, Poddar S: Changing epidemiology of SARS-CoV in the context of COVID-19 pandemic. J. Prev. Med. Hyg. 2020; 61(2): E130–E136. PubMed Abstract | Publisher Full Text | Free Full Text
2. World Health Organization: WHO Coronavirus (COVID-19) Dashboard.2022. Accessed 8 February 2022. Reference Source
3. Indonesia TF of C-19: Distribution Map of COVID-19 Indonesia.2022. Accessed 8 February 2022. Reference Source
4. Organisation WH: Advice for the public: Coronavirus disease (COVID-19). World Health Organization; 2021. Accessed 8 February 2022. Reference Source
5. Minister of Home Affairs of the Republic of Indonesia: Regulation of (the) Minister of Home Affairs of Republic Indonesia. 2021; Vol. 4: pp. 1–2.
6. Task Force of COVID-19 Indonesia. Memahami Perilaku dan InformasiTepatuntukMencegah Penularan COVID-19. Covid19.Go. Id. 2020. Accessed February 8, 2022. Reference Source
7. Task Force of COVID-19 Indonesia: Monitoring of Health Protocol Compliance.2021. Accessed 8 February 2022. Reference Source
8. Nguyen NPT, Hoang TD, Tran VT, et al.: Preventive behaviour of Vietnamese people in response to the COVID-19 pandemic. PLoS One. 2020; 15(9 September): 1–11. PubMed Abstract | Publisher Full Text | Free Full Text
9. Lim JW, Gonzalez P, Wang-Letzkus MF, et al.: Understanding the cultural health belief model influencing health behaviours and health-related quality of life between Latina and Asian-American breast cancer survivors. Support Care Cancer. 2009; 17(9): 1137–1147. PubMed Abstract | Publisher Full Text
10. Clark C, Davila A, Regis M, et al.: Predictors of COVID-19 voluntary compliance behaviours: An international investigation. Glob. Transitions. 2020; 2: 76–82. PubMed Abstract | Publisher Full Text | Free Full Text
11. Jewell NP: Predictive Mathematical Models of the COVID-19 Pandemic Underlying Principles and Value of Projections. Am. Med. Assoc. J. 2020; 323(19): 1893–1894. Publisher Full Text
12. Jose R, Narendran M, Bindu A, et al.: Public perception and preparedness for the pandemic COVID 19: A Health Belief Model approach. Clin. Epidemiol. Glob. Heal. 2021; 9(9): 41–46. PubMed Abstract | Publisher Full Text | Free Full Text
13. Purnama SG, Susanna D: Attitude to COVID-19 Prevention with Large-Scale Social Restrictions (PSBB) in Indonesia: Partial Least Squares Structural Equation Modeling. Front. Public Health. 2020; 8(October): 1–10. PubMed Abstract | Publisher Full Text | Free Full Text
14. Susanna D, Widyamurti W, Eryando T: Implementasi Of Social Policy Distancing In Effort Prevention COVID-19 In Indonesia. figshare. Journal Contribution. 2023. Publisher Full Text
15. Basu A: Structural equation modelling with lavaan: a tutorial and an intuitive introduction.2021 [cited 2023 Jun 20]. Reference Source Reference Source
16. Mahindarathne PP: Assessing COVID-19 preventive behaviours using the health belief model: A Sri Lankan study. J. Taibah Univ. Med. Sci. 2021; 16(6): 914–919. PubMed Abstract | Publisher Full Text | Free Full Text
17. Glanz K, Rimer B, Viswanath K: Health Behavior and Health Education. COT, editor. Fourth Ed.John Wiley & Sons, Ltd.; 2008.
18. Rea LM, Parker RA: Designing and Conducting Survey Research. A Comprehensive Guide. Jossey-Bass A Wiley Brand; 2014.
19. Hair JF, Anderson RE, Tatham RL, et al.: Multivariate Data Analysis. Eighth Ed.Annabel Ainscow; 2019.
20. Kline RB: Principles and Practice of Structural Equation Modelling. Fourth Ed.The Guilford Press; 2016.
21. Tadesse T, Alemu T, Amogne G, et al.: Predictors of coronavirus disease 2019 (Covid-19) prevention practices using health belief model among employees in Addis Ababa, Ethiopia, 2020. Infect. Drug Resist. 2020; 13: 3751–3761. Publisher Full Text
22. Shahnazi H, Ahmadi-Livani M, Pahlavanzadeh B, et al.: Assessing preventive health behaviors from COVID-19: a cross-sectional study with health belief model in Golestan Province, Northern of Iran. Infect. Dis. Poverty. 2020; 9(1): 157–159. PubMed Abstract | Publisher Full Text | Free Full Text
23. Tong KK, Chen JH, Yu EY, et al.: Adherence to COVID-19 Precautionary Measures: Applying the Health Belief Model and Generalised Social Beliefs to a Probability Community Sample. Appl. Psychol. Heal. Well-Being. 2020; 12(4): 1205–1223. PubMed Abstract | Publisher Full Text | Free Full Text
24. Kim S, Kim S: Analysis of the impact of health beliefs and resource factors on preventive behaviors against the Covid-19 pandemic. Int. J. Environ. Res. Public Health. 2020; 17(22): 1–21. PubMed Abstract | Publisher Full Text | Free Full Text
25. Park JH, Cheong HK, Son DY, et al.: Perceptions and behaviors related to hand hygiene for the prevention of H1N1 influenza transmission among Korean university students during the peak pandemic period. BMC Infect. Dis. 2010; 10: 1–8. PubMed Abstract | Publisher Full Text | Free Full Text
26. Khalafalla HE, Tumambeng MZ, Halawi MHA, et al.: COVID-19 Vaccine Hesitancy Prevalence and Predictors among the Students of Jazan University, Saudi Arabia Using the Health Belief Model: A Cross-Sectional Study. Vaccines. 2022; 10(2). PubMed Abstract | Publisher Full Text | Free Full Text
27. Wong LP, Alias H, Wong PF, et al.: The use of the health belief model to assess predictors of intent to receive the COVID-19 vaccine and willingness to pay. Hum. Vaccin. Immunother. 2020; 16(9): 2204–2214. PubMed Abstract | Publisher Full Text | Free Full Text
28. Hatteberg SJ, Kollath-Cattano C: Applying the health belief model to examine college students’ early-stage adoption of non-pharmaceutical interventions in response to COVID-19. J. Am. Coll. Heal. 2022; 1–6. PubMed Abstract | Publisher Full Text
29. Du M, Tao L, Liu J: Association between risk perception and influenza vaccine hesitancy for children among reproductive women in China during the COVID-19 pandemic: a national online survey. BMC Public Health. 2022; 22(1): 310–385. PubMed Abstract | Publisher Full Text | Free Full Text
30. Ejeta LT, Ardalan A, Paton D, et al.: Predictors of community preparedness for flood in Dire-Dawa town, Eastern Ethiopia: Applying an adapted version of Health Belief Model. Int. J. Disaster Risk Reduct. 2016; 19(September): 341–354. Publisher Full Text
31. Karimy M, Azarpira H, Araban M: Using health belief model constructs to examine differences in adherence to pap test recommendations among Iranian women. Asian Pac. J. Cancer Prev. 2017; 18(5): 1389–1394. PubMed Abstract | Publisher Full Text | Free Full Text
32. Kim HS, Ahn J, No JK: Applying the health belief model to college students’ health behaviour. Nutr. Res. Pract. 2012; 6(6): 551–558. Publisher Full Text
33. Wong MCS, Wong ELY, Huang J, et al.: Acceptance of the COVID-19 vaccine based on the health belief model: A population-based survey in Hong Kong.2020; 12.083(January): 1148–1156.
34. Ansari-Moghaddam A, Seraji M, Sharafi Z, et al.: The protection motivation theory for predicting intention of COVID-19 vaccination in Iran: a structural equation modeling approach. BMC Public Health. 2021; 21(1): 1–9. PubMed Abstract | Publisher Full Text | Free Full Text
35. Muslih M, Susanti HD, Rias YA, et al.: Knowledge, attitude, and practice of Indonesian residents toward Covid-19: A cross-sectional survey. Int. J. Environ. Res. Public Health. 2021; 18(9). Publisher Full Text
36. Yanti B, Wahyudi E, Wahiduddin W, et al.: Community Knowledge, Attitudes, and Behavior Towards Social Distancing Policy as Prevention Transmission of Covid-19 in Indonesia. J. AdmKesehatIndones. 2020; 8(2): 4. Publisher Full Text
37. Gao H, Hu R, Yin L, et al.: Knowledge, attitudes and practices of the Chinese public with respect to coronavirus disease (COVID-19): An online cross-sectional survey. BMC Public Health. 2020; 20: 1–8. PubMed Abstract | Publisher Full Text | Free Full Text
38. Hussain A, Garima T, Singh BM, et al.: Knowledge, attitudes, and practices towards COVID-19 among Nepalese Residents: A quick online cross-sectional survey. Asian J. Med. Sci. 2020; 11(3): 6–11. Publisher Full Text
39. Zhong BL, Luo W, Li HM, et al.: Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: A quick online cross-sectional survey. Int. J. Biol. Sci. 2020; 16(10): 1745–1752. PubMed Abstract | Publisher Full Text | Free Full Text
40. Azlan AA, Hamzah MR, Sern TJ, et al.: Public knowledge, attitudes and practices towards COVID-19: A cross-sectional study in Malaysia. PLoS One. 2020; 15(5): 1–15. PubMed Abstract | Publisher Full Text | Free Full Text
41. Narayana G, Pradeepkumar B, Dasaratha J: Knowledge, perception, and practices towards COVID-19 pandemic among the general public of India: A cross-sectional online survey. Curr. Med. Res. Pract. 2020; 10(2020): 153–159. PubMed Abstract | Publisher Full Text | Free Full Text
42. Daragmeh A, Sági J, Zéman Z: Continuous intention to use e-wallet in the context of the covid-19 pandemic: Integrating the health belief model (hbm) and technology continuous theory (tct). J. Open Innov.: Technol. Mark. Complex. 2021 May 12; 7(2): 132. Publisher Full Text
43. Hussain MW, Mirza T, Hassan MM: Impact of COVID-19 Pandemic on the Human Behavior. Int. J. Educ. Manag. Eng. 2020; 10(6): 35–61. Publisher Full Text
44. Ajilore K, Atakiti I, Onyenankeya K: College students’ knowledge, attitudes and adherence to public service announcements on Ebola in Nigeria: Suggestions for improving future Ebola prevention education programmes. Health Educ. J. 2017; 76(6): 648–660. Publisher Full Text
45. Martinez-Lacoba R, Pardo-Garcia I, Escribano-Sotos F: Aging, dependence, and long-term care: A systematic review of employment creation. Inquiry. 2021 Dec 16; 58: 00469580211062426. PubMed Abstract | Publisher Full Text | Free Full Text
46. Tachfouti N, Slama K, Berraho M, et al.: The impact of knowledge and attitudes on adherence to tuberculosis treatment: A case-control study in a Moroccan region. Pan Afr. Med. J. 2012; 12(1): 1–8. Publisher Full Text
47. Susanna D, Widyamurti W, Eryando T: Data for: Community perception and COVID-19 prevention. Dataset. Dryad. 2023. Publisher Full Text
48. Susanna D, Widyamurti W, Eryando T: IMPLEMENTATION OF SOCIAL POLICY DISTANCING IN EFFORT PREVENTION COVID-19 IN INDONESIA. Journal Contribution. figshare. 2023. Publisher Full Text

Comments on this article Comments (1)

Version 3

VERSION 3 PUBLISHED 03 Oct 2024

Revised

Comment

Version 1

VERSION 1 PUBLISHED 10 Aug 2023

Discussion is closed on this version, please comment on the latest version above.

Author Response 18 Jan 2024

Tris Eryando, Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia

18 Jan 2024

Author Response

Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be ... Continue reading Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract.

Methodology
Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity.
Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population.
Thus, this study is vulnerable to information and selection bias.
All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study.

Study and sample
As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark.
As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity.
Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study.

Conceptual model
This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper,

Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document
Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract.

Methodology
Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity.
Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population.
Thus, this study is vulnerable to information and selection bias.
All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study.

Study and sample
As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark.
As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity.
Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study.

Conceptual model
This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper,

Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document
Competing Interests: No competing interests were disclosed. Close
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Author details Author details

Tris Eryando
Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Visualization, Writing – Review & Editing

Tiopan Sipahutar
Roles: Data Curation, Formal Analysis, Methodology, Visualization, Writing – Original Draft Preparation

Sandeep Poddar
Roles: Visualization, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This research was funded by Universitas Indonesia under contract number NKB-630/UN2.RST/HKP.05.00/2022
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Eryando T, Sipahutar T and Poddar S. Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 1; peer review: 2 approved with reservations]. F1000Research 2023, 12:966 (https://doi.org/10.12688/f1000research.135262.1)

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Reviewer Report 22 Sep 2023

Rotimi Oguntayo, Department of Psychology, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, Chihuahua, Mexico

Approved with Reservations

https://doi.org/10.5256/f1000research.148373.r201070

Abstract (Minor Revision): In the conclusion section, significant results have been attached to preventive behavior and community perceptions of the severity of COVID-19 disease, but to support objective data, it is necessary to attach data in the form of accurate numbers in accordance with the findings in the field. In the results section, you mentioned that there is a relationship between self-efficacy and perceived influence on preventive behavior. How did you come to the conclusion that these variables are related?

Methodology (Major Revision): This is an interesting study because it is a large-scale study of the country of Indonesia. I have high hopes that this research will be successfully indexed so that it can be used as a reference by other studies in information about Community preventive behavior and perception of the severity of COVID-19 disease in Indonesia. As with other research articles published in various journals, your research article needs significant improvement to enhance its readability and credibility. You collected data through a Google Form distributed via various social media channels, resulting in thousands of responses. However, it is essential to address potential biases in data collection. How did you ensure that respondents didn't fill out the Google Form multiple times? Was there strict control over the form submissions? Additionally, you mentioned that respondents were spread across Indonesia. How did you ensure even distribution among Indonesia's 38 provinces? Please provide details or references for this distribution method. If you were unable to meet up with any of these criteria, list them as your study limitations.

Study and Sample (Minor Revision): This research aims to reach the Indonesian public, including contributions from social media influencers. To ensure the research's representativeness, please clarify how you ensured that the sample accurately represents all Indonesians. Your respondents are identified as Indonesian citizens, but how did you verify their citizenship? What measures were in place to prevent participation by non-Indonesian residents? Consider addressing these potential sources of bias. If you were unable to meet up with any of these criteria, list them as your study limitations.

Conceptual Model (Major Revision): This research employs the Health Belief Model (HBM) as the primary theoretical framework. It is crucial to emphasize that Structural Equation Modeling (SEM) relies on a well-established theory to validate models using real data. The direction of relationships in the model must align with theoretical support. Additionally, theoretical underpinnings are essential in determining the appropriate sample size for SEM and ensuring sample adequacy when testing a theory-based model. Please provide a more comprehensive, precise and concise theoretical foundation for your SEM analysis.

Grammatical Errors (Minor Revision): Correct the English writing errors in the article generally. Moreover, consider updating the COVID-19 incidence rate beyond the end of 2021, extending it into 2022, to enhance the study's accuracy and encompassing quality.

Results: The results section requires elaboration and clarification to explain how you arrived at the conclusion regarding the relationship between self-efficacy and perceived influence on preventive behavior for readers who not statistic expert to be able to read and understand while retaining the journal format.

Discussion: Expand on the implications of the results and how they relate to the theoretical framework. Discuss the broader significance of your findings in the context of COVID-19 prevention in Indonesia and HBM concept for clinical practice in local context. Also, consider inclusion of more recent similar COVID-19 incidence data in other country to buttress your results in this section.

Conclusion with Limitations and Implications: Summarize the key findings, discuss the study’s limitations as suggested above, and provide implications for clinical practice or public health initiatives, and future research to ascertain replicability of your work.

Indexing of the Paper: The indexing of the paper is contingent upon addressing the major revisions outlined in the methodology and conceptual model sections. Minor revisions should also be completed in the abstract, study and sample, grammatical errors, and the inclusion of more recent COVID-19 incidence data in the discussion section. Once these revisions are made, the paper will be reevaluated for indexing.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Psychology (Crises Prevention and management)

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

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Author Response 28 Dec 2023

Tris Eryando, Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia

28 Dec 2023

Author Response

Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be ... Continue reading Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract.

Methodology
Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity.
Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population.
Thus, this study is vulnerable to information and selection bias.
All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study.

Study and sample
As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark.
As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity.
Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study.

Conceptual model
This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper,

Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document
Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract.

Methodology
Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity.
Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population.
Thus, this study is vulnerable to information and selection bias.
All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study.

Study and sample
As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark.
As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity.
Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study.

Conceptual model
This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper,

Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 28 Dec 2023

Tris Eryando, Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia

28 Dec 2023

Author Response

Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be ... Continue reading Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract.

Methodology
Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity.
Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population.
Thus, this study is vulnerable to information and selection bias.
All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study.

Study and sample
As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark.
As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity.
Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study.

Conceptual model
This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper,

Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document
Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract.

Methodology
Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity.
Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population.
Thus, this study is vulnerable to information and selection bias.
All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study.

Study and sample
As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark.
As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity.
Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study.

Conceptual model
This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper,

Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document
Competing Interests: No competing interests were disclosed. Close
Report a concern

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Reviewer Report 13 Sep 2023

Husnah Husnah, Department of Nutrition, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia

Approved with Reservations

https://doi.org/10.5256/f1000research.148373.r195846

1. Abstract (minor revision)

In the conclusion section, significant results have been attached to preventive behavior and community perceptions of the severity of COVID-19 disease, but to support objective data, it is necessary to attach data in the form of accurate numbers in accordance with the findings in the field. In the results section, you mentioned that there is a relationship between self efficacy and perceived influence on preventive behavior. How did you come to the conclusion that these variables are related?

2. Methods (major revision)

This is an interesting study because it is a large-scale study of the country of Indonesia. I have high hopes that this research will be successfully indexed so that it can be used as a reference by other studies in information about Community preventive behavior and perception on the severity of COVID-19 disease in Indonesia. As with other research articles that have been published in various journals, of course your research article needs to be improved to make it worth reading.

Your research obtained data from a google form that was distributed through various social media. You managed to get thousands of responses. But I am curious how can you ensure that one respondent does not fill in the google form more than once? Is there strict control over the filling of google forms by respondents? I hope that your data does not become biased because of this.

Then you mentioned that the respondents were spread across Indonesia. How do you ensure that the respondents are indeed spread evenly across Indonesia, which consists of 38 provinces? Perhaps you have your own reference in determining the distribution throughout Indonesia as you mentioned, please explain.

3. Study and Sample (minor revision)

This research expands to the Indonesian public with contributions from social media influencers. How do you ensure this research is representative of all Indonesians? Respondents participating in your research are Indonesian citizens. How do you ensure that the people filling in the data are Indonesian citizens? What if a foreigner residing in Indonesia receives and fills out a questionnaire from your research, how do you know?

4. Conceptual Model (major revision)

This research uses HBM as the main reference. What should be a common concern in using SEM must be based on a certain theory. This is because the function of SEM is to confirm the model formed on real data obtained in the field. In addition, the determination of the direction of the relationship built in the model must be theoretically corroborated. This will also affect the determination of the amount of data or research samples that are suitable for use in SEM and feasible in terms of sample adequacy in testing a theory-based model.

5. Minor revision

Correct the English writing errors in the article. I have marked them on the linked annotated article. For example, in the questionnaire table in the methods section, and in the discussion section. The COVID-19 incidence rate has been mentioned from mid-2021 to the end of 2021. To add to the accuracy of this study, the incidence rate of COVID-19 until 2022 should also be attached.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Nutrition Department

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Comments on this article Comments (1)

Version 3

VERSION 3 PUBLISHED 03 Oct 2024

Revised

Comment

Version 1

VERSION 1 PUBLISHED 10 Aug 2023

Discussion is closed on this version, please comment on the latest version above.

Author Response 18 Jan 2024

Tris Eryando, Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia

18 Jan 2024

Author Response

Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be ... Continue reading Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract.

Methodology
Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity.
Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population.
Thus, this study is vulnerable to information and selection bias.
All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study.

Study and sample
As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark.
As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity.
Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study.

Conceptual model
This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper,

Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document
Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract.

Methodology
Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity.
Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population.
Thus, this study is vulnerable to information and selection bias.
All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study.

Study and sample
As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark.
As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity.
Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study.

Conceptual model
This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper,

Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document
Competing Interests: No competing interests were disclosed. Close
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Discussion is closed on this version, please comment on the latest version above.

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Version 2 (revision) 28 Dec 23		read	read
Version 1 10 Aug 23	read	read

Husnah Husnah, Syiah Kuala University, Banda Aceh, Indonesia
Rotimi Oguntayo, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, Mexico
Valentina Lucia La Rosa, University of Catania, Catania, Italy

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Reviewer Report

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21 Oct 2024 | for Version 3

Husnah Husnah, Department of Nutrition, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia

1 Views Cite this report Responses(0)

Approved With Reservations

Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations]

Based on the latest revision that I have studied, there are several suggestions for improvement.

A. Background
The background and methods are appropriate

B. Results
1. The results of the research need to be supplemented with data about the number of respondents written down, most of which should be called numbers, not just percentages. Example: More than halfof respondents should write 901 (50%) instead of 1802
2. The value of a significant relationship should be written down, not just p<0.05

C. Conclusion
In the sentence respondents have a low perception of vulnerability (84%) and severity (67%), it is better to add the number of respondents, then write the percentage

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Nutrition Department

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8 Views

05 Sep 2024 | for Version 2

Rotimi Oguntayo, Department of Psychology, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, Chihuahua, Mexico

8 Views Cite this report Responses(0)

Approved

Dear Authors!
Thank you for attending to all my questions, and the amendment made to the article is commendable. At this point, I recommend that this work be indexed.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Psychology (Crises Prevention and management)

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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24 Jul 2024 | for Version 2

Valentina Lucia La Rosa, Department of Educational Sciences, University of Catania, Catania, Italy

14 Views Cite this report Responses(1)

Approved With Reservations

The manuscript “Community Preventive Behaviour and Perception on the Severity of COVID-19 Disease in Indonesia, 2021-2022: Structural Equation Modelling" provides a valuable contribution to the understanding of COVID-19 perceptions and preventive behaviors in Indonesia. The use of HBM and SEM provides a robust framework for their analysis. However, addressing the revisions outlined above will significantly strengthen the manuscript and improve its suitability for indexed.

The Authors should provide a broader theoretical basis for their SEM analysis. The HBM is a well-established model, but the authors need to explicitly link their research questions and hypotheses to the specific constructs of the HBM. In addition, they should discuss how the HBM influenced their choice of variables and the relationships tested in their SEM model.
The Authors need to provide more details about their data collection methods. In particular, they should address how they ensured that respondents did not complete the Google form multiple times and how they ensured an even distribution of respondents across the 38 provinces of Indonesia. If these measures were not taken, this point should be acknowledged as a limitation of the study.
The goodness-of-fit indices suggest that the proposed model does not adequately fit the data. The authors should consider revising the model by exploring alternative constructs or pathways that may better represent the empirical data.
The Authors are required to provide a detailed explanation of the steps taken to address issues with the model fit and any changes that were made to improve the model.
The Authors should expand the discussion of the implications of their findings, with particular focus on the non significant predictors, and discuss how these findings contribute to our understanding of preventive behaviors in the context of COVID-19.
I suggest including references to studies that have examined the perceived health risk associated with COVID-19 in different populations, such as adolescents and college students, to provide a broader context and comparison for your findings. Some of these studies include: 10.3389/fpsyg.2020.559951; 10.3390/ejihpe13080108.
The manuscript should be thoroughly proofread to correct grammatical errors and improve its readability.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Commodari E, La Rosa VL: Adolescents in Quarantine During COVID-19 Pandemic in Italy: Perceived Health Risk, Beliefs, Psychological Experiences and Expectations for the Future.Front Psychol. 2020; 11: 559951 PubMed Abstract | Publisher Full Text
2. La Rosa VL, Commodari E: University Experience during the First Two Waves of COVID-19: Students' Experiences and Psychological Wellbeing.Eur J Investig Health Psychol Educ. 2023; 13 (8): 1477-1490 PubMed Abstract | Publisher Full Text

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Development in the life span and the impact of critical events such as the COVID-19 pandemic

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Author Response

03 Oct 2024

Tris Eryando, Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia

Response to no. 1: We propose a comprehensive approach to understanding behavior change using the Health Belief Model (HBM), as opposed to focusing to understand behaviour change partially. The HBM offers a holistic perspective on behaviour change processes. However, when utilizing HBM as a theoretical framework, there were latent variables that did not directly observed. These latent variables are represented by various indicators/observe variable. Consequently, Structural Equation Modelling (SEM) is the most suitable analytical method for examining relationships between variables (latent variable and measurement/indicator) within the HBM framework, as it allows for the analysis of both latent variables and the overall model as a single, cohesive unit. We added the additional statement in the article.
Response to no. 2: We did not restrict respondents to just entry the form one time. However, once the data collected and stored in to the database, researcher did data checking particularly to identify double/triple number. The researcher selected one data randomly and deleted the rest with the same number. There was possibility that one person will entry multiple times using different identity. We added the additional statement in the article.
Response to no. 3: Unfortunately, we didn’t develop or explore the alternative construct after finding that the model did not fit the data since the aim of this study was to examine changes in community behaviour to prevent local COVID-19 transmissions and changes in community perceptions about the severity level of COVID-19. This study also intended to prove whether the construct proposed can predict the behaviour or not and the result showed that it could not. We did not re-construct the model considering Hair et al. suggestion that modifying the model should not change the model’s structure significantly.
Response to no. 4: In order to propose the good model, we have explained the one by one in the paper which include identifying degree of freedom, construct validity, discriminant validity, construct reliability, dependency and goodness of fit index. During the process, we excluded 2 variable that disturbed the data. However, when the model builds and tested with the goodness of fit index, we did not continue to re-structure the model. We refer to Hair et al. suggestion that modifying the model should not change the model’s structure significantly. We already stated it in the paper.
We added reference as suggested.

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

28 Views

22 Sep 2023 | for Version 1

Rotimi Oguntayo, Department of Psychology, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, Chihuahua, Mexico

28 Views Cite this report Responses(1)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Psychology (Crises Prevention and management)

Respond to this report

Responses (1)

Author Response

28 Dec 2023

Tris Eryando, Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia

Thank you for your valuable inputs to this paper.
We already revised almost all part according to your input. Following are our response:

Abstract
The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract.

Methodology
Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity.
Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population.
Thus, this study is vulnerable to information and selection bias.
All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study.

Study and sample
As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark.
As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity.
Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study.

Conceptual model
This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper,

Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document

View more View less

Competing Interests

No competing interests were disclosed.

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Reviewer Report

23 Views

13 Sep 2023 | for Version 1

Husnah Husnah, Department of Nutrition, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia

23 Views Cite this report Responses(0)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

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[1] 1. Bhattacharya S, Basu P, Poddar S: Changing epidemiology of SARS-CoV in the context of COVID-19 pandemic. J. Prev. Med. Hyg. 2020; 61(2): E130–E136. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. World Health Organization: WHO Coronavirus (COVID-19) Dashboard.2022. Accessed 8 February 2022. Reference Source

[3] 3. Indonesia TF of C-19: Distribution Map of COVID-19 Indonesia.2022. Accessed 8 February 2022. Reference Source

[4] 4. Organisation WH: Advice for the public: Coronavirus disease (COVID-19). World Health Organization; 2021. Accessed 8 February 2022. Reference Source

[5] 5. Minister of Home Affairs of the Republic of Indonesia: Regulation of (the) Minister of Home Affairs of Republic Indonesia. 2021; Vol. 4: pp. 1–2.

[6] 6. Task Force of COVID-19 Indonesia. Memahami Perilaku dan InformasiTepatuntukMencegah Penularan COVID-19. Covid19.Go. Id. 2020. Accessed February 8, 2022. Reference Source

[7] 7. Task Force of COVID-19 Indonesia: Monitoring of Health Protocol Compliance.2021. Accessed 8 February 2022. Reference Source

[8] 8. Nguyen NPT, Hoang TD, Tran VT, et al.: Preventive behaviour of Vietnamese people in response to the COVID-19 pandemic. PLoS One. 2020; 15(9 September): 1–11. PubMed Abstract | Publisher Full Text | Free Full Text

[9] 9. Lim JW, Gonzalez P, Wang-Letzkus MF, et al.: Understanding the cultural health belief model influencing health behaviours and health-related quality of life between Latina and Asian-American breast cancer survivors. Support Care Cancer. 2009; 17(9): 1137–1147. PubMed Abstract | Publisher Full Text

[10] 10. Clark C, Davila A, Regis M, et al.: Predictors of COVID-19 voluntary compliance behaviours: An international investigation. Glob. Transitions. 2020; 2: 76–82. PubMed Abstract | Publisher Full Text | Free Full Text

[11] 11. Jewell NP: Predictive Mathematical Models of the COVID-19 Pandemic Underlying Principles and Value of Projections. Am. Med. Assoc. J. 2020; 323(19): 1893–1894. Publisher Full Text

[12] 12. Jose R, Narendran M, Bindu A, et al.: Public perception and preparedness for the pandemic COVID 19: A Health Belief Model approach. Clin. Epidemiol. Glob. Heal. 2021; 9(9): 41–46. PubMed Abstract | Publisher Full Text | Free Full Text

[13] 13. Purnama SG, Susanna D: Attitude to COVID-19 Prevention with Large-Scale Social Restrictions (PSBB) in Indonesia: Partial Least Squares Structural Equation Modeling. Front. Public Health. 2020; 8(October): 1–10. PubMed Abstract | Publisher Full Text | Free Full Text

[14] 14. Susanna D, Widyamurti W, Eryando T: Implementasi Of Social Policy Distancing In Effort Prevention COVID-19 In Indonesia. figshare. Journal Contribution. 2023. Publisher Full Text

[15] 15. Basu A: Structural equation modelling with lavaan: a tutorial and an intuitive introduction.2021 [cited 2023 Jun 20]. Reference Source Reference Source

[16] 16. Mahindarathne PP: Assessing COVID-19 preventive behaviours using the health belief model: A Sri Lankan study. J. Taibah Univ. Med. Sci. 2021; 16(6): 914–919. PubMed Abstract | Publisher Full Text | Free Full Text

[17] 17. Glanz K, Rimer B, Viswanath K: Health Behavior and Health Education. COT, editor. Fourth Ed.John Wiley & Sons, Ltd.; 2008.

[18] 18. Rea LM, Parker RA: Designing and Conducting Survey Research. A Comprehensive Guide. Jossey-Bass A Wiley Brand; 2014.

[19] 19. Hair JF, Anderson RE, Tatham RL, et al.: Multivariate Data Analysis. Eighth Ed.Annabel Ainscow; 2019.

[20] 20. Kline RB: Principles and Practice of Structural Equation Modelling. Fourth Ed.The Guilford Press; 2016.

[21] 21. Tadesse T, Alemu T, Amogne G, et al.: Predictors of coronavirus disease 2019 (Covid-19) prevention practices using health belief model among employees in Addis Ababa, Ethiopia, 2020. Infect. Drug Resist. 2020; 13: 3751–3761. Publisher Full Text

[22] 22. Shahnazi H, Ahmadi-Livani M, Pahlavanzadeh B, et al.: Assessing preventive health behaviors from COVID-19: a cross-sectional study with health belief model in Golestan Province, Northern of Iran. Infect. Dis. Poverty. 2020; 9(1): 157–159. PubMed Abstract | Publisher Full Text | Free Full Text

[23] 23. Tong KK, Chen JH, Yu EY, et al.: Adherence to COVID-19 Precautionary Measures: Applying the Health Belief Model and Generalised Social Beliefs to a Probability Community Sample. Appl. Psychol. Heal. Well-Being. 2020; 12(4): 1205–1223. PubMed Abstract | Publisher Full Text | Free Full Text

[24] 24. Kim S, Kim S: Analysis of the impact of health beliefs and resource factors on preventive behaviors against the Covid-19 pandemic. Int. J. Environ. Res. Public Health. 2020; 17(22): 1–21. PubMed Abstract | Publisher Full Text | Free Full Text

[25] 25. Park JH, Cheong HK, Son DY, et al.: Perceptions and behaviors related to hand hygiene for the prevention of H1N1 influenza transmission among Korean university students during the peak pandemic period. BMC Infect. Dis. 2010; 10: 1–8. PubMed Abstract | Publisher Full Text | Free Full Text

[26] 26. Khalafalla HE, Tumambeng MZ, Halawi MHA, et al.: COVID-19 Vaccine Hesitancy Prevalence and Predictors among the Students of Jazan University, Saudi Arabia Using the Health Belief Model: A Cross-Sectional Study. Vaccines. 2022; 10(2). PubMed Abstract | Publisher Full Text | Free Full Text

[27] 27. Wong LP, Alias H, Wong PF, et al.: The use of the health belief model to assess predictors of intent to receive the COVID-19 vaccine and willingness to pay. Hum. Vaccin. Immunother. 2020; 16(9): 2204–2214. PubMed Abstract | Publisher Full Text | Free Full Text

[28] 28. Hatteberg SJ, Kollath-Cattano C: Applying the health belief model to examine college students’ early-stage adoption of non-pharmaceutical interventions in response to COVID-19. J. Am. Coll. Heal. 2022; 1–6. PubMed Abstract | Publisher Full Text

[29] 29. Du M, Tao L, Liu J: Association between risk perception and influenza vaccine hesitancy for children among reproductive women in China during the COVID-19 pandemic: a national online survey. BMC Public Health. 2022; 22(1): 310–385. PubMed Abstract | Publisher Full Text | Free Full Text

[30] 30. Ejeta LT, Ardalan A, Paton D, et al.: Predictors of community preparedness for flood in Dire-Dawa town, Eastern Ethiopia: Applying an adapted version of Health Belief Model. Int. J. Disaster Risk Reduct. 2016; 19(September): 341–354. Publisher Full Text

[31] 31. Karimy M, Azarpira H, Araban M: Using health belief model constructs to examine differences in adherence to pap test recommendations among Iranian women. Asian Pac. J. Cancer Prev. 2017; 18(5): 1389–1394. PubMed Abstract | Publisher Full Text | Free Full Text

[32] 32. Kim HS, Ahn J, No JK: Applying the health belief model to college students’ health behaviour. Nutr. Res. Pract. 2012; 6(6): 551–558. Publisher Full Text

[33] 33. Wong MCS, Wong ELY, Huang J, et al.: Acceptance of the COVID-19 vaccine based on the health belief model: A population-based survey in Hong Kong.2020; 12.083(January): 1148–1156.

[34] 34. Ansari-Moghaddam A, Seraji M, Sharafi Z, et al.: The protection motivation theory for predicting intention of COVID-19 vaccination in Iran: a structural equation modeling approach. BMC Public Health. 2021; 21(1): 1–9. PubMed Abstract | Publisher Full Text | Free Full Text

[35] 35. Muslih M, Susanti HD, Rias YA, et al.: Knowledge, attitude, and practice of Indonesian residents toward Covid-19: A cross-sectional survey. Int. J. Environ. Res. Public Health. 2021; 18(9). Publisher Full Text

[36] 36. Yanti B, Wahyudi E, Wahiduddin W, et al.: Community Knowledge, Attitudes, and Behavior Towards Social Distancing Policy as Prevention Transmission of Covid-19 in Indonesia. J. AdmKesehatIndones. 2020; 8(2): 4. Publisher Full Text

[37] 37. Gao H, Hu R, Yin L, et al.: Knowledge, attitudes and practices of the Chinese public with respect to coronavirus disease (COVID-19): An online cross-sectional survey. BMC Public Health. 2020; 20: 1–8. PubMed Abstract | Publisher Full Text | Free Full Text

[38] 38. Hussain A, Garima T, Singh BM, et al.: Knowledge, attitudes, and practices towards COVID-19 among Nepalese Residents: A quick online cross-sectional survey. Asian J. Med. Sci. 2020; 11(3): 6–11. Publisher Full Text

[39] 39. Zhong BL, Luo W, Li HM, et al.: Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: A quick online cross-sectional survey. Int. J. Biol. Sci. 2020; 16(10): 1745–1752. PubMed Abstract | Publisher Full Text | Free Full Text

[40] 40. Azlan AA, Hamzah MR, Sern TJ, et al.: Public knowledge, attitudes and practices towards COVID-19: A cross-sectional study in Malaysia. PLoS One. 2020; 15(5): 1–15. PubMed Abstract | Publisher Full Text | Free Full Text

[41] 41. Narayana G, Pradeepkumar B, Dasaratha J: Knowledge, perception, and practices towards COVID-19 pandemic among the general public of India: A cross-sectional online survey. Curr. Med. Res. Pract. 2020; 10(2020): 153–159. PubMed Abstract | Publisher Full Text | Free Full Text

[42] 42. Daragmeh A, Sági J, Zéman Z: Continuous intention to use e-wallet in the context of the covid-19 pandemic: Integrating the health belief model (hbm) and technology continuous theory (tct). J. Open Innov.: Technol. Mark. Complex. 2021 May 12; 7(2): 132. Publisher Full Text

[43] 43. Hussain MW, Mirza T, Hassan MM: Impact of COVID-19 Pandemic on the Human Behavior. Int. J. Educ. Manag. Eng. 2020; 10(6): 35–61. Publisher Full Text

[44] 44. Ajilore K, Atakiti I, Onyenankeya K: College students’ knowledge, attitudes and adherence to public service announcements on Ebola in Nigeria: Suggestions for improving future Ebola prevention education programmes. Health Educ. J. 2017; 76(6): 648–660. Publisher Full Text

[45] 45. Martinez-Lacoba R, Pardo-Garcia I, Escribano-Sotos F: Aging, dependence, and long-term care: A systematic review of employment creation. Inquiry. 2021 Dec 16; 58: 00469580211062426. PubMed Abstract | Publisher Full Text | Free Full Text

[46] 46. Tachfouti N, Slama K, Berraho M, et al.: The impact of knowledge and attitudes on adherence to tuberculosis treatment: A case-control study in a Moroccan region. Pan Afr. Med. J. 2012; 12(1): 1–8. Publisher Full Text

[47] 47. Susanna D, Widyamurti W, Eryando T: Data for: Community perception and COVID-19 prevention. Dataset. Dryad. 2023. Publisher Full Text

[48] 48. Susanna D, Widyamurti W, Eryando T: IMPLEMENTATION OF SOCIAL POLICY DISTANCING IN EFFORT PREVENTION COVID-19 IN INDONESIA. Journal Contribution. figshare. 2023. Publisher Full Text

Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling

Abstract

Keywords

Introduction

Methods

Study design and samples

Conceptual model

Individual characteristics and community perception of COVID-19

Community preventive behavior against COVID-19 transmission

Figure 1. Proposed structural model of community preventive behavior against COVID-19.14

Data analysis

Table 1. The observed variables.

Results

Table 2. Respondents’ characteristics.

Table 3. Distribution (%) of perceived items (N=1802).

SEM results

Identify degree of freedom of structural model

Construct validity

Table 4. Results of the discriminant validity test.

Construct reliability

Structural model validity

Dependency test

Table 5. Results of the SEM statistical test.

Table 6. Goodness-of-fit index results.

Discussion

Study limitations

Conclusions

Ethics approval and consent to participate

Consent for publication

Author contributions

Data availability

Acknowledgments

References

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Figure 1. Proposed structural model of community preventive behavior against COVID-19.¹⁴