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Perceived social support and self-stigma as factors of COVID-19 booster vaccination behavior and intention via cognitive coping and emotion regulation among people infected with COVID-19 in Hong Kong

BMC Public Health volume 25, Article number: 659 (2025) Cite this article

Abstract

Background

It is imperative to promote behavior/intention of taking up booster COVID-19 vaccination (BI-BV) among people who have ever contracted COVID-19 (PECC). The aims were to investigate the prevalence of BI-BV and its associations with perceived social support. Guided by the stress coping theory, we tested mediators between perceived social support and BI-BV via self-stigma, active coping, and maladaptive emotion regulation (rumination and catastrophizing).

Methods

A random population-based telephone survey was conducted among adult PECC having completed the primary series of COVID-19 vaccination prior to the diagnosis; 230 participants were interviewed from June to August 2022 during the fifth (last) major outbreak in Hong Kong. The associations between the independent variables and BI-BV were tested by logistic regression analysis. A structural equation model (SEM) tested the indirect effects of the latent variables of self-stigma, active coping, and maladaptive emotion regulation between the latent variable of perceived social support and BI-BV.

Results

The prevalence of BI-BV was 62.2%. It was associated with age, marital status, full-time employment, and chronic disease status. The logistic regression analysis found that BI-BV was positively associated with perceived social support (ORc = 1.31, 95% CI: 1.12– 1.54), active coping (ORc = 1.40, 95% CI: 1.10– 1.79), rumination (ORc = 1.75, 95% CI: 1.13– 2.70), and catastrophizing (ORc = 3.12, 95% CI: 1.49– 6.51) and negatively associated with self-stigma (ORc = 0.80, 95% CI: 0.72– 0.88). In the SEM analysis, the positive association between perceived social support and BI-BV was fully mediated: 1) via self-stigma (β = 0.07, 95% CI: 0.03– 0.14), 2) via active coping (β = 0.06, 95% CI: 0.02– 0.12), and 3) via self-stigma and then active coping (β = 0.01, 95% CI: 0.002– 0.04). Two of these indirect paths involved active coping. The indirect paths involving maladaptive emotion regulation were all non-significant.

Conclusions

Perceived social support was associated with BI-BV, and was mediated via self-stigma, active coping, and serially self-stigma then active coping but not emotion maladaptation. The data supported the stress cognitive coping model in explaining the association between perceived social support and BI-BV. Interventions promoting BI-BV may consider modifying the observed significant factors. Future longitudinal studies are warranted to confirm the findings.

Peer Review reports

Introduction

Although COVID-19 is no longer a pandemic, it remains globally prevalent. The prevalence of COVID-19 reinfection was 28% when the BQ.1/BQ.1.1 variants were involved [1]. Reinfection increases the risks of death, hospitalization [2], and severity of long COVID symptoms (e.g., lung scarring, heart diseases, neurological effects, and psychological consequences) [3]. Booster vaccination can increase immunity against reinfection [4, 5]. According to a meta-analysis of 91 studies that investigated 158,478 records, the risk of reinfection after > = 12 months from the first infection was lower among vaccinated individuals than unvaccinated individuals, although the reinfection rates were low in both groups [6]. The World Health Organization and some country authorities recommend people to take up booster COVID-19 vaccination six months after diagnosis [7,8,9]. It is important to promote booster vaccination in specific sub-populations (e.g., older people). Vaccinated people who have ever been infected with COVID-19 (PECC) is a special population that warrants attention. The infection experience of the PECC might have affected their confidence in the vaccines whereas such confidence was a factor of vaccination [10]. The experience of COVID-19 infection may also modify perceptions on risk of infection, consequences of infection, and perceived efficacy of vaccination, which were potential determinants of booster vaccination [11]. According to social marketing principles, segmentation and tailored strategies would improve effectiveness of health promotion [12]. Identification of psychosocial factors of behavior/intention of taking up booster COVID-19 vaccination (BI-BV) among PECC is hence essential. One study conducted in Italy reported that female sex, negative affective state, and lower perceived risk were associated with reluctance to take up the vaccines after contracting COVID-19 [13]. Another study conducted in Hong Kong explored some psychological factors associated with BI-BV among PECC [14]. No other similar studies were found in literature.

The stress coping theory was used as this study’s framework [15]. It postulates that when facing a stressor, an individual’s coping resources would alleviate the level of stress and affect his/her coping strategies, which would in turn affect his/her coping responses and coping outcomes [15]. In this study, perceived social support was considered as a coping resource, self-stigma as a stressor related to COVID-19, active coping as a cognitive coping strategy, and BI-BV as a coping response. In literature, perceived social support is a coping resource [15, 16] that affects health behaviors (e.g., physical exercise, dietary behavior) [17,18,19], and COVID-19 vaccination [20, 21]; it was also associated with various types of coping strategies [22, 23], including active coping [24]. However, the association between social support and booster vaccination among vaccinated PECC remains unclear. Public stigma was common during the COVID-19 pandemic [25, 26] and the SARS outbreak [27]. Self-stigma related to COVID-19 refers to internalization of the public stigma [28]. It is a prevalent stressor which was positively associated with mental distress [29, 30], and was also negatively associated with COVID-19 vaccination intention/behavior [31] and active coping in previous studies [32]. There is a dearth of studies looking at the relationship between self-stigma and booster vaccination behavior/intention among vaccinated PECC.

Emotion regulation and coping strategies are closely related but distinctive constructs that evolve separately [33]; they involve different strategies (e.g., active coping for coping and rumination for emotion regulation) [34, 35] and measurement scales [36, 37]. Emotion regulation refers to how an individual respond to emotionally arousing situations; such regulation may be adaptive or maladaptive, leading to better or worse emotional states, respectively [38]. Coping refers to how an individual manages specific demands arising from stressful situations, which is not limited to emotional difficulties and is hence more general than emotion regulation [37]. Coping is usually a volitional and controlled process while emotion regulation can be automated or uncontrolled [39]. Furthermore, some researchers suggested that emotion regulation is a subset of coping (emotional coping) [40]; others highlighted their differences [41]; a few studies even found minimal or non-significant associations between the two [42]. Since both processes involve regulations under stressful situations [39], the present study integrated both coping (active coping) and maladaptive emotion regulation (rumination and catastrophizing) into the stress-coping framework. It contends that the associations between perceived social support/self-stigma and BI-BV would be mediated through both a ‘cognitive coping process’ (i.e., via active coping) and an ‘emotion regulation process’ (i.e., via maladaptive emotion regulation). Maladaptive emotion regulation is a subset of emotion regulation which may lead to harmful/ineffective consequences [43]. Rumination (how people concentrate on the ideas and emotions connected to unfavorable events) and catastrophizing (exaggerations of the seriousness of an event) are two common modes of maladaptive emotion regulation [37]. As previous studies found that perceived social support was associated with self-stigma [44,45,46] whereas emotion regulation was associated with coping strategies [47, 48], it is plausible that self-stigma, emotion regulation, and active coping would mediate between perceived social support and BI-BV serially. The hypothetical model is presented in Supplementary Fig. 1.

The study was conducted in Hong Kong, where various control measures against COVID-19 (e.g., social distancing, facemask use and provision of free COVID-19 vaccination) were implemented since February 2020 [49]. As of December 31, 2021, the total number of COVID-19 cases and deaths was about 12,649 and 213, respectively [50]. The severe fifth-wave outbreak occurred from late December, 2021 to January, 2023 [51], during which about 1.4 million new cases and about 13,000 deaths were reported [52]. This study was conducted from June 20 to August 2, 2022, hence during an acute phase of the COVID-19 pandemic.

This study investigated the prevalence of BI-BV among PECC in Hong Kong, China, and tested the significance of some potential factors of BI-BV, i.e., perceived social support, self-stigma related to COVID-19 infection, active coping, and maladaptive emotion regulation (rumination and catastrophizing). Using structural equation model (SEM) analysis, two single-mediator paths between the latent variable perceived social support and BI-BV via the latent variables of active coping/maladaptive emotion regulation and two others between the latent variable of self-stigma and BI-BV via the latent variables of active coping/maladaptive emotion regulation were tested. Three additional serial mediation paths were tested: a) perceived social support → self-stigma → active coping → BI-BV, b) perceived social support → self-stigma → maladaptive emotion regulation → BI-BV), and c) perceived social support → self-stigma → maladaptive emotion regulation → active coping → BI-BV (see Supplementary Fig. 1). To our knowledge, no previous studies have tested such mediation paths.

Methods

Study design

A cross-sectional random, population-based telephone survey was conducted in Hong Kong, China from June 20 to August 2, 2022.

Participants

Inclusion criteria included: (1) Chinese aged 18 years or older, (2) PECC status determined by the screening question: “Have you been tested positive for COVID-19 via the Nucleic Acid Amplification Test (NAAT) or Rapid Antigen Test (RAT)? (Yes/No response options)”, (3) completion of the primary series of vaccination (at least two doses of the COVID-19 vaccines) prior to the COVID-19 diagnosis. Regarding exclusion criteria, as the epidemiology, social context, and experience of COVID-19 infection was changing over time [10], factors of BI-BV were time-dependent. Thus, exclusion criteria were: 1) PECC not being diagnosed during the fifth (last) major COVID-19 outbreak wave in Hong Kong, i.e., excluding diagnosis made prior to February l, 2022, 2) those having taken up four doses of vaccines prior to the diagnosis as free fifth dose vaccination was not then available in Hong Kong, 3) those having difficulties in understanding and/or responding to the questions according to the judgement of the trained interviewers, and 4) non Chinese speakers.

Data collection

Telephone numbers were randomly generated from a fixed-line phone directory. Interviews, which took about 20 min to complete, were conducted between 5 and 10 pm to avoid oversampling of unemployed people. The eligible household member whose birthday was closest to the interview date was invited to participate in the study. At least three attempts were made to follow on the unanswered phone calls before classifying them as invalid numbers. The interviews were conducted in Chinese. Briefing was provided by the well-trained interviewers to ensure participants were informed about the anonymous and voluntary nature of the study. Verbal informed consent was obtained from the participants. The interviewers signed a form declaring that they had gone through the proper briefing/consent procedures. Previous studies have used similar methodologies [14, 53, 54]. No incentives were given to the participants.

A total of 469 valid telephone contacts were made, of which 280 participants completed the interview (response rate: 280/469 = 59.7%); 50 cases were excluded from data analysis (incompletion of primary series [n = 41], diagnosed before February 1, 2022 [n = 3], having taken up four doses of vaccines prior to the COVID-19 diagnosis [n = 1], and no response to the question about vaccination behavior/intention [n = 5]). The final effective sample size was 230.

Measures

Background factors

Background information was collected (sex, age, education level, marital status, employment status, whether living alone, whether having a religious belief, and chronic disease status). Regarding chronic disease status, participants were asked: “Do you have the following chronic diseases (hypertension, diabetes, heart disease, lung disease, kidney disease, liver disease, respiratory disease, cancer, others)?” The response categories for each item were “yes”, “no”, “don’t know”, or “refuse to answer”. Those having at least one “yes” answer was classified as having one or more types of chronic disease.

Behavior/intention of taking up booster dose of COVID-19 vaccination (BI-BV)

It was assessed by the item: “After receiving your COVID-19 diagnosis, have you taken up a booster dose of COVID-19 vaccination or would you do so in the future? (1: neither having taken up nor intended to take up the booster vaccine; 2: having taken up the booster vaccine after the COVID-19 diagnosis; 3: had not taken up the booster vaccine but intended to do so).The three responses were recoded into a binary variable by combining 2) and 3) into the ‘individuals with either booster vaccination behavior or intention group’ (i.e., those with BI-BV) and the ‘individuals with neither booster vaccination behavior nor intention group’ (i.e., those without BI-BV).

Perceived social support

Perceived social support was assessed in two dimensions (perceived emotional support and perceived instrumental support), which have frequently been used to assess the level of perceived social support [55]. The two questions were: “Your family and friends would be available when you need emotional support or need someone to talk to” and “When you need practical help in life (such as financial needs), your family and friends will give you enough support”. Participants rated the items on two Likert scales, ranging from 1 (highly disagree) to 7 (highly agree). Previous studies have used similar questions to assess perceived social support [56, 57]. In this study, the Cronbach’s alpha of the scale was 0.85. A summation score of the two items was used to represent the level of perceived social support in the univariate logistic regression analysis.

Self-stigma related to COVID-19 infection

COVID-19-related self-stigma was assessed by using a 3-item scale [58]. The three items assessed the levels of reluctance to disclose COVID-19 infection to others, perceived negative views from others because of his/her COVID-19 infection status, and perceived public discrimination toward people infected with COVID-19. Likert scales were used to rate the item responses (1 = highly disagree to 5 = highly agree). In this study, the Cronbach’s alpha of the scale was 0.96. A summation score of the three items was used to represent the level of self-stigma in the univariate logistic regression analysis.

Active coping

Active coping was assessed by using the 2-item subscale of the Brief Coping Orientation to Problems Experienced (brief COPE) inventory [36]. The two items were “I have been concentrating my efforts on doing something about my situation” and “I have been taking actions to try to improve my situation”. The subscale has been used in COVID-19 related research [59, 60]. The items were rated by 4-point Likert scales, ranging from 1 = “I haven’t been doing this at all” to 4 = “I have been doing this a lot”. In this study, the Cronbach’s alpha of the scale was 0.94. A score summing up the two item scores was used in the univariate logistic regression analysis.

Maladaptive emotion regulation

Maladaptive emotion regulation was assessed by the rumination and catastrophizing subscales of the Cognitive Emotion Regulation Questionnaire-Short Version (CERQ-short) [37]. The two subscales have been used to investigate COVID-19 related maladaptive emotion regulations [61, 62]. Each of them has two items (“You constantly recall the pandemic as well as your feelings about the pandemic”, and “You indulge in your thoughts and feelings about the pandemic” for rumination, and “You constantly think about how terrible the pandemic is” and “Sometimes you feel like the disaster is coming” for catastrophizing). Each item was assessed on a 5-point Likert scale from 1 (never) to 5 (always). The two subscales were formed by summing up their two respective item scores. They were used in the univariate logistic regression analysis. In this study, the Cronbach’s alphas of the rumination and catastrophizing subscales were 0.97 and 0.81, respectively.

Statistical analysis

BI-BV was used as the binary dependent variable. Univariable logistic regression analysis was performed to examine the individual associations between the background variables/independent variables (perceived social support, self-stigma, active coping, rumination, and catastrophizing) and BI-BV. Crude odds ratios (ORc) and respective 95% confidence intervals (CIs) were reported. The confidence level of 95% was used to define statistical significance.

A structural equation model (SEM) was fit to assess whether self-stigma, active coping, and maladaptive emotion regulation would mediate between perceived social support and BI-BV, using the weighted least squared mean and variance adjusted (WLSMV) estimator [63]. Three latent variables were constructed for perceived social support, self-stigma, active coping, whereas maladaptive emotion regulation was a second-order latent variable based on the rumination and catastrophizing subscales [64]. Satisfactory model fit indices included χ2/df < 5, comparative fit index (CFI) ≥ 0.90, Tucker-Lewis index (TLI) ≥ 0.90, and root mean square error of approximation (RMSEA) ≤ 0.08 [65]. The bootstrapping method (n = 2000) was conducted to test the significance of the indirect effects. An indirect effect was statistically significant when its 95% CI did not include zero. The SEM analysis was performed using Mplus 8.3, while the other tests were conducted using Stata 17.0 (Stata Corp, College Station, TX, USA).

Results

Participants’ characteristics

Sixty-three percent of the participants were female; 60.9% aged between 31–60 years; 92.6% did not live alone; 70.9% were currently married; 26.1% had received college or above education; 53.0% worked fulltime. Furthermore, 77.8% did not have a religious belief; 71.7% reported no chronic diseases. The prevalence of BI-BV was 62.2%, with 19 participants (8.3%) having taken up a booster vaccine after their COVID-19 diagnosis and 124 participants (53.9%) not having taken up the booster vaccines since diagnosis but intended to do so. The mean score was 5.70 for the self-stigma scale (SD = 2.97; range = 3–15), 11.18 for the perceived social support scale (SD = 1.80; range = 2–14), 6.69 for the active coping subscale (SD = 1.13; range = 2–8), 2.87 for the rumination subscale (SD = 1.43; range = 2–10), and 2.55 for the catastrophizing subscale (SD = 1.16; range = 2–10), respectively. The data are presented in Table 1.

Table 1 Participants’ characteristics (n = 230)
Full size table

Factors of BI-BV

People of older age (> 60 versus 18–30: ORc = 8.33, 95% CI: 2.92–23.81), being currently married (versus currently unmarried: ORc = 1.80, 95% CI: 1.01– 3.20), not engaged in fulltime work (part-time versus fulltime: ORc = 2.97, 95% CI: 1.11– 7.93; others versus fulltime: ORc = 2.60, 95% CI: 1.42– 4.75), and having chronic disease(s) (versus no/don’t know: ORc = 2.58, 95% CI: 1.34– 4.96) were more likely than others to have reported BI-BV. The factors of sex, education level, whether living alone, and religious belief were statistically non-significant. Positive associations with BI-BV were found for the factors of perceived social support (ORc = 1.31, 95% CI: 1.12– 1.54), active coping (ORc = 1.40, 95% CI: 1.10– 1.79), rumination (ORc = 1.75, 95% CI: 1.13 2.70), and catastrophizing (ORc = 3.12, 95% CI: 1.49– 6.51). A negative association was found for the factor of self-stigma (ORc = 0.80, 95% CI: 0.72– 0.88). The results are presented in Table 2.

Table 2 Factors of behavior/intention of taking up booster COVID-19 vaccination
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Structural equation modeling analysis

The confirmatory factor analysis reported satisfactory model fit indices (χ2/df = 42.967/36 = 1.19 < 2; RMSEA 0.03; CFI 0.99; TLI 0.99) for the four latent variables (perceived social support, self-stigma, active coping, and maladaptive emotion regulation). The factor loadings of the latent variables range from 0.68 to 0.98 (all had p < 0.05). The results indicated that the measurement model was suitable for the SEM analysis. Information about loadings of these latent variables are presented in Supplementary Fig. 2.

The SEM results are presented in Fig. 1. Background factors were adjusted for. The model yields satisfactory model fit indices (χ2/df = 95/88 = 1.08 < 2; RMSEA = 0.019, CFI = 0.981, TLI = 0.969, SRMR = 0.047). Several significant mediations were identified, supporting some of the initial hypotheses. First, the indirect effect of perceived social support on BI-BV via active coping was statistically significant (β = 0.06, 95% CI: 0.02–0.12), i.e., perceived social support was positively associated with active coping (β = 0.29, 95% CI: 0.15–0.44), which was in turn positively associated with BI-BV (β = 0.19, 95% CI: 0.04–0.34). The mediation effect size was 21%. Second, the indirect effect between perceived social support and BI-BV via self-stigma was statistically significant (β = 0.07, 95% CI: 0.03– 0.14), i.e., perceived social support was negatively associated with self-stigma (β = −0.28, 95% CI: −0.43– −0.11), which was in turn negatively associated with BI-BV (β = −0.25, 95% CI: −0.42– −0.11). The mediation effect size was 27%. Third, the serial indirect path of higher perceived social support → lower self-stigma → higher active coping → higher BI-BV was also statistically significant (β = 0.01, 95% CI: 0.002 – 0.04), i.e., perceived social support was negatively associated with self-stigma (β = −0.28, 95% CI: −0.43 – −0.11), which was in turn negatively associated with active coping (β = −0.22, 95% CI: −0.36 – −0.09), which was in turn positively associated with BI-BV (β = 0.19, 95% CI: 0.04– 0.34). The mediation effect size was 5%. The total mediation size of the three significant indirect paths was thus 53%. Notably, three other indirect paths, all of which involved emotion regulation as the mediator, were statistically non-significant (perceived social support → maladaptive emotion regulation → BI-BV; perceived social support → self-stigma → maladaptive emotion regulation → BI-BV; perceived social support → self-stigma → maladaptive emotion regulation → active coping → BI-BV). Maladaptive emotion regulation was significantly associated with BI-BV (β = 0.37, 95% CI: 0.20– 0.55). Furthermore, the direct effect from perceived social support to BI-BV (β = 0.08, 95% CI: −0.07, 0.25) was statistically non-significant.

Fig. 1
figure 1

The structural equation model testing the mediation effects. Note: Standardized coefficients are presented. BI-BV = Behavior/intention of taking up booster COVID-19 vaccination

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Discussion

This is one of the few studies investigating BI-BV among PECC who completed the primary series of COVID-19 vaccination prior to their diagnosis. To summarize the main findings, the prevalence of BI-BV among PECC was 62.2%. According to the SEM analysis, perceived social support and self-stigma were positively and negatively associated with BI-BV, respectively. Interestingly, the association between perceived social support and BI-BV was fully mediated via 1) self-stigma, 2) active coping, and 3) self-stigma then active coping (a serial mediation). Two of these indirect paths involved the cognitive strategy of active coping. However, the other three mediation indirect paths involving maladaptive emotion regulation were all statistically non-significant. Unexpectedly, in the SEM model, maladaptive emotion regulation was positively associated with BI-BV but was not significantly associated with perceived social support and self-stigma in the SEM.

The suboptimal prevalence of BI-BV observed in this study was lower than the reported prevalence of the primary series vaccination during the pandemic previously recorded in some general populations [66]. Notably, only 19 out of the 230 participants had taken up the vaccination. One reason was that behavioral intention often does not translate into action [67, 68]. Another reason is that as most of the participants were recently infected (within the last six months), it might be too early for them to take up booster vaccination, considering a 6-month interval between diagnosis and vaccination recommended by the professionals. As the pandemic had ended, less people would maintain the health beliefs that are supportive of booster vaccination (e.g., perceived severity of COVID-19), health promotion of vaccination is going to be an uphill challenge. In this study, older and currently married people were more likely than their counterparts to report BI-BV, corroborating previous studies conducted in Hong Kong and other countries [69,70,71]. Plausible explanations are that older people might have stronger concerns about the consequences of reinfection whereas currently married people might want to protect their spouse and children through vaccination. Furthermore, participants not working full time showed higher BI-BV, plausibly because of their older age and/or having more time to take up vaccination. Health promotion may pay more attention to such groups with lower BI-BV.

A few reasons may partially explain the observed suboptimal prevalence of BI-BV. First, there were no mandate vaccination policies for booster vaccination among PECC in Hong Kong, who thus had no obligation to disclose their booster vaccination status to anyone. Hence, there was no external pressure urging PECC to take up the vaccines although booster vaccination was encouraged by the government and free vaccination was available to all Hong Kong residents at the time of the study. Second, PECC might believe that they had acquired natural immunity that was adequately protective against reinfection [72]. Third, as all the participants were infected with COVID-19 despite completion of the primary vaccination series, some of them might cast doubts on the efficacy of the booster vaccine [14]. In health promotion, PECC should hence be convinced that hybrid immunities resulted from infection and vaccination are important [73] and that the world authorities have recommended booster vaccination for PECC [7,8,9].

The stress coping model [15] involving cognitive coping (active coping) in terms of BI-BV was well supported by this study, as the indirect paths involving self-stigma and active coping fully mediated the association between perceived social support and BI-BV. The findings suggest that an increase in perceived social support and a reduction in self-stigma which would increase active coping and then BI-BV. The other observed serial mediation involving active coping further supported the stress coping model involving cognitive coping (perceived social support → self-stigma → active coping → BI-BV). To promote BI-BV, it is possible to enhance perceived social support provided by family members, significant others, and co-workers [74]. Self-stigma can be reduced via various interventions, such as psychoeducation, cognitive behavioral therapy, social skills training, etc. [75]. Interventions also need to reduce self-stigma by convincing PECC that COVID-19 infection is not a fault. Active coping can be promoted by using the problem‐solving model [76, 77]. The findings hence have both practical and theoretical significance.

In contrast to the supportive findings regarding the cognitive stress coping model, the mediation paths based on the maladaptive emotion regulation (rumination and catastrophizing) model were all statistically non-significant. Such findings suggest that the cognitive stress coping model worked better than the emotion regulation model in explaining the association between perceived social support and BI-BV. In addition, this study found that active coping was not significantly associated with maladaptive emotion regulation. The two findings suggest that in this case, cognitive coping and emotion regulation are two separate processes. Also, there is no study looking at the effects of coping and emotion regulation jointly in the same model. The findings have thus added knowledge to the discussion comparing the coping and emotion regulation processes. Future studies are warranted to confirm the findings.

Furthermore, there is a dearth of studies investigating the role of emotion regulation in determining booster vaccination among PECC. The only study found in literature reported that rumination was negatively associated with vaccination behavior [78]. In contrast, the latent variable of maladaptive emotion regulation was positively, instead of negatively, associated with BI-BV in this study. An explanation is that rumination/catastrophizing may be seen as either a maladaptive emotion regulation [37] or an expression of fear [79]. The former but not necessarily the latter implies a negative association between maladaptive emotion regulation and BI-BV. According to the fear appeal approach [80], ‘fight’ (removal of the source of fear) or ‘flight’ responses (avoidance of thinking about the situation) may be evoked in a fearful context. Booster vaccination may be seen as a ‘fight response’ to fear, instead of being a maladaptive emotion regulation outcome which should be negatively associated with BI-BV; rumination/catastrophizing may be seen as an expression of fear that initiated a fight response that would lead to vaccination. This perspective may partially explain the ‘unexpected’ positive association between maladaptation and BI-BV. As active coping is an adaptive process, its positive association with BI-BV is expected and vaccination can be interpreted as an adaptive response.

Corroborating previous studies [20, 21, 81, 82] and supporting the initial hypotheses, this study finds that perceived social support and active coping were positively associated with BI-BV and self-stigma was negatively associated with BI-BV. No study has investigated the potential effects of self-stigma due to infection on BI-BV among PECC although a previous study looked at the similar effects in the general population [31]. As COVID-19 affected almost every aspects of life [83], some PECC might have internalized the public stigma, and worried about infecting often or bringing trouble to other people. Stigma and self-stigma regarding infectious diseases (e.g., HIV, SARS, and H1N1) were common [27, 84,85,86] and were barriers against health service utilization [87]. It is uncertain whether the level of self-stigma has subsided in the post-pandemic period; further research is needed.

A published paper was based on the same dataset that was analyzed in the present paper [14]. It investigated associations between cognitive factors (e.g. perceived susceptibility to COVID-19)/emotional factors (e.g. illness concern) and intention to take COVID-19 booster vaccine among PECC in Hong Kong [14], whereas the present paper looked at the associations between a different set of psychosocial factors (perceived social support and stigma) and behavior/intention of booster vaccination as well as mediators between the associations via active coping and emotional regulation among PECC. The dependent variables of the two papers were different—the current paper looked at both behavior and intention whereas the other paper only looked at intention of booster vaccination. Also, the current but not the former paper included vaccinated PECC in the sample. These two papers thus presented different perspectives. Another study was conducted in Hong Kong from June 20 to July 18, 2022 [88], which investigated multidimensional determinants (e.g., perceptions, information exposure, family influences, and mental distress) of first-dose COVID-19 vaccination behavior among older adults aged 60 years or above in the Hong Kong general population. The current and the other two published papers thus involve different objectives and study populations; they together present a more comprehensive picture about COVID-19 vaccination in Hong Kong.

This study has innovative findings but also several limitations. First, BI-BV might have been over-reported as it is a socially desirable behavior. Second, the moderate response rate of this study might lead to selection bias; characteristics of non-respondents were, however, unavailable for comparisons. Third, a single question was used to assess BI-BV, although similar questions were commonly asked in previous studies [89, 90]. The dependent variable combined vaccination behavior with intention to indicate the overall vaccination tendency after receiving a COVID-19 diagnosis. Fourth, the study did not record the exact duration since diagnosis. Yet, as the survey was conducted from June to August 2022 during the major fifth wave outbreak that peaked after February 2022, the majority of the participants had been diagnosed for less than six months. As vaccination within six months since diagnosis was not recommended to PECC, only 19 participants had taken up a booster vaccination after receiving their COVID-19 diagnosis and were possibly diagnosed during the early phase of the outbreak and interviewed during the late phase of the study period. A sensitivity analysis was conducted by removing such cases and the same findings were obtained. With a larger sample of the two groups, a comparison between factors of intention of booster vaccination between the groups could be conducted. The overall sample size of 230 was not large, but it fits into the rule of thumb for SEM analysis that there should be at least 10 cases per observed variables [91]. Fifth, recall bias was possible; however, its size should be minimum as the recall period was relatively short (< 6 months) and the only question that might be subject to recall bias was about post-diagnosis vaccination (which was a pivotal event) within the last six months. Sixth, temporal or causal inferences are not feasible due to the cross-sectional study design; longitudinal studies are warranted. Seventh, the study was conducted during a major outbreak in Hong Kong, so it is uncertain whether the studied relationships would be stable and valid over time and in the post-pandemic stage. Similarly, as the study was conducted among PECC in Hong Kong, caution should be taken when generalizing the results to other populations. Eighth, the betas of the significant indirect path were relatively small. Researchers have, however, pointed out that indirect effect of such sizes may be seen as ‘small but not negligible’ when it has theoretical/practical implications [92, 93]. Notably, the mediation effect sizes of the three significant indirect paths were 27%, 21%, and 5%; the total mediation size of 53% was substantial; the findings are certainly useful in understanding the mechanisms between perceived social support and BI-BV. Last, there are other unstudied coping strategies apart from active coping. Cautions should be taken when claiming the usefulness of the stress cognitive coping model, as other types of coping strategies may have different effects on BI-BV. Similarly, apart from rumination and catastrophizing, there are also other unstudied maladaptive/adaptive emotion coping responses that might play different roles in explaining BI-BV.

Conclusions

This study identified significant associations between perceived social support/self-stigma/active coping/maladaptive emotion regulation and BI-BV. Except the unexpected positive association involving maladaptive coping, the other significant associations with BI-BV followed the expected directions. A key finding was that self-stigma and active coping significantly, both individually and serially, mediated between perceived social support and BI-BV. The indirect paths involving emotion regulation were however, all statistically non-significant, signifying that coping (active coping) and maladaptive emotion regulation (rumination/catastrophizing) were two separate processes; the former worked better than the latter in explaining the mechanism between perceived social support and BI-BV. These findings are novel. Interventions are needed to increase BI-BV among PECC by increasing perceived social support, reducing self-stigma, and improving active coping. Reminders should be disseminated to the general public that COVID-19 remains prevalent and harmful whereas booster vaccination is safe and efficacious. Longitudinal studies are needed to verify the findings.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

BI-BV:

Behavior/intention of taking up booster COVID-19 vaccination

PECC:

People who have ever contracted COVID-19

RMSEA:

Root mean square error of approximation

CFI:

Comparative fit index

TLI:

Tucker-Lewis index

SEM:

Structural equation model

CI:

Confidence interval

ORc:

Crude odds ratio

df:

Degree of freedom

References

  1. Ma KC. Trends in Laboratory-Confirmed SARS-CoV-2 Reinfections and Associated Hospitalizations and Deaths Among Adults Aged≥ 18 Years—18 US Jurisdictions, September 2021–December 2022. MMWR Morb Mortal Wkly Rep. 2023;72:683.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Bowe B, Xie Y, Al-Aly Z. Acute and postacute sequelae associated with SARS-CoV-2 reinfection. Nat Med. 2022;28(11):2398–405.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wanjari M, Late S, Sharma R, Munjewar P. Long-term pulmonary and extra-pulmonary consequences of COVID-19: A comprehensive review of current evidence and future perspectives. Narra J. 2023;3(2):e156.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Hall V, Foulkes S, Insalata F, Kirwan P, Saei A, Atti A, et al. Protection against SARS-CoV-2 after Covid-19 vaccination and previous infection. N Engl J Med. 2022;386(13):1207–20.

    Article  CAS  PubMed  Google Scholar 

  5. Hammerman A, Sergienko R, Friger M, Beckenstein T, Peretz A, Netzer D, et al. Effectiveness of the BNT162b2 vaccine after recovery from Covid-19. N Engl J Med. 2022;386(13):1221–9.

    Article  CAS  PubMed  Google Scholar 

  6. Flacco ME, Acuti Martellucci C, Baccolini V, De Vito C, Renzi E, Villari P, et al. Risk of reinfection and disease after SARS-CoV-2 primary infection: Meta-analysis. Eur J Clin Invest. 2022;52(10):e13845.

    Article  CAS  PubMed  Google Scholar 

  7. Staying Up to Date with COVID-19 Vaccines Centers for Disease Control and Prevention 2024 [updated 3 Oct 2024; cited 2025 Jan 4]. Available from: https://www.cdc.gov/covid/vaccines/stay-up-to-date.html?CDC_AAref_Val=https://www.cdc.gov/coronavirus/2019-ncov/vaccines/stay-up-to-date.html.

  8. The State Council of the People's Republic of China. Second booster shot available for at-risk people 2022 [updated 14 Dec 2022 cited 2023 Nov 20]. Available from: https://english.www.gov.cn/statecouncil/ministries/202212/14/content_WS63996aa0c6d0a757729e46f7.html.

  9. World Health Organization. Interim statement on the use of additional booster doses of Emergency Use Listed mRNA vaccines against COVID-19 2023 [cited 2023 Nov 20]. Available from: https://www.who.int/news/item/17-05-2022-interim-statement-on-the-use-of-additional-booster-doses-of-emergency-use-listed-mrna-vaccines-against-covid-19.

  10. Yuan J, Xu Y, Wong IOL, Lam WWT, Ni MY, Cowling BJ, et al. Dynamic predictors of COVID-19 vaccination uptake and their interconnections over two years in Hong Kong. Nat Commun. 2024;15(1):290.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Nguyen KH, Huang J, Mansfield K, Corlin L, Allen JD. COVID-19 vaccination coverage, behaviors, and intentions among adults with previous diagnosis, United States. Emerg Infect Dis. 2022;28(3):631.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Grier S, Bryant CA. Social marketing in public health. Annu Rev Public Health. 2005;26(1):319–39.

    Article  PubMed  Google Scholar 

  13. Caserotti M, Gavaruzzi T, Girardi P, Tasso A, Buizza C, Candini V, et al. Who is likely to vacillate in their COVID-19 vaccination decision? Free-riding intention and post-positive reluctance. Prev Med. 2022;154:106885.

    Article  PubMed  Google Scholar 

  14. Yu Y, Zhang X, Lau MM, Lau JT. The intention to get COVID-19 booster vaccination and its association with cognitive and emotional factors: A survey of Chinese COVID-19 infected people in Hong Kong. Vaccine. 2024;42(2):206–12.

    Article  CAS  PubMed  Google Scholar 

  15. Lazarus RS, Folkman S. Stress, appraisal, and coping. New York: Springer publishing company; 1984.

  16. Schwarzer R, Knoll N. Functional roles of social support within the stress and coping process: A theoretical and empirical overview. Int J Psychol. 2007;42(4):243–52.

    Article  Google Scholar 

  17. Jeon B, Lee H, Shon C, Kim N, Kim A, Park J, et al. The association of social support with health status and health behavior among rural aged population. J Agric Med Commun Health. 2009;31(4):13–23.

  18. Allgöwer A, Wardle J, Steptoe A. Depressive symptoms, social support, and personal health behaviors in young men and women. Health Psychol. 2001;20(3):223.

    Article  PubMed  Google Scholar 

  19. Yoshikawa A, Smith ML, Lee S, Towne SD, Ory MG. The role of improved social support for healthy eating in a lifestyle intervention: Texercise Select. Public Health Nutr. 2021;24(1):146–56.

    Article  PubMed  Google Scholar 

  20. Galanis P, Katsiroumpa A, Sourtzi P, Siskou O, Konstantakopoulou O, Katsoulas T, et al. Social Support Mediates the Relationship between COVID-19-Related Burnout and Booster Vaccination Willingness among Fully Vaccinated Nurses. Vaccines. 2022;11(1):46.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Jaspal R, Breakwell GM. Social support, perceived risk and the likelihood of COVID-19 testing and vaccination: cross-sectional data from the United Kingdom. Curr Psychol. 2022;41(1):492–504.

    Article  PubMed  Google Scholar 

  22. Holahan CJ, Holahan CK, Moos RH, Brennan PL. Psychosocial, adjustment in patients reporting cardiac illness. Psychol Health. 1997;12(3):345–59.

    Article  Google Scholar 

  23. Shen YE. Relationships between self-efficacy, social support and stress coping strategies in Chinese primary and secondary school teachers. Stress Health J Int Soc Invest Stress. 2009;25(2):129–38.

    Article  Google Scholar 

  24. Chapman T, Chi TC. Perceived Social Support mediates the link between optimism and active coping. J Behav Soc Sci. 2017;4(2):57–65.

    Google Scholar 

  25. Abuhammad S, Alzoubi KH, Khabour O. Fear of COVID-19 and stigmatization towards infected people among Jordanian people. Int J Clin Pract. 2021;75(4):e13899.

    Article  CAS  PubMed  Google Scholar 

  26. Haddad C, Bou Malhab S, Malaeb D, Sacre H, Saadeh D, Mourtada V, et al. Stigma toward people with COVID-19 among the Lebanese population: a cross-sectional study of correlates and mediating effects. BMC Psychol. 2021;9:1–15.

    Google Scholar 

  27. Lau JTF, Xilin Y, Eric W, Tsui HY. Prevalence and factors associated with social avoidance of recovered SARS patients in the Hong Kong general population. Health Educ Res. 2006;5:662–73.

  28. Corrigan PW, Rao D. On the self-stigma of mental illness: Stages, disclosure, and strategies for change. Can J Psychiatry. 2012;57(8):464–9.

    Article  PubMed  Google Scholar 

  29. Chang K-C, Lin C-Y, Chang C-C, Ting S-Y, Cheng C-M, Wang J-D. Psychological distress mediated the effects of self-stigma on quality of life in opioid-dependent individuals: A cross-sectional study. PLoS ONE. 2019;14(2):e0211033.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Huang P-C, Lee C-H, Griffiths MD, O’Brien KS, Lin Y-C, Gan WY, et al. Sequentially mediated effects of weight-related self-stigma and psychological distress in the association between perceived weight stigma and food addiction among Taiwanese university students: a cross-sectional study. J Eat Disord. 2022;10(1):177.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Mo PK, She R, Yu Y, Li L, Yang Q, Lin J, et al. Resilience and intention of healthcare workers in China to receive a COVID-19 vaccination: The mediating role of life satisfaction and stigma. J Adv Nurs. 2022;78(8):2327–38.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kleim B, Vauth R, Adam G, Stieglitz R-D, Hayward P, Corrigan P. Perceived stigma predicts low self-efficacy and poor coping in schizophrenia. J Ment Health. 2008;17(5):482–91.

    Article  Google Scholar 

  33. John OP, Gross JJ. Individual Differences in Emotion Regulation. In: Gross JJ, editor. Handbook of emotion regulation. New York: The Guilford Press; 2007. p.351–72.

  34. Aldao A, Jazaieri H, Goldin PR, Gross JJ. Adaptive and maladaptive emotion regulation strategies: Interactive effects during CBT for social anxiety disorder. J Anxiety Disord. 2014;28(4):382–9.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Folkman S, Moskowitz JT. Coping: Pitfalls and promise. Annu Rev Psychol. 2004;55(1):745–74.

    Article  PubMed  Google Scholar 

  36. Carver CS. You want to measure coping but your protocol’too long: Consider the brief cope. Int J Behav Med. 1997;4(1):92–100.

    Article  CAS  PubMed  Google Scholar 

  37. Garnefski N, Kraaij V, Spinhoven P. Negative life events, cognitive emotion regulation and emotional problems. Personality Individ Differ. 2001;30(8):1311–27.

    Article  Google Scholar 

  38. Thompson RA. Emotion regulation: A theme in search of definition. Monogr Soc Res Child Dev. 1994;59:25–52.

    Article  CAS  PubMed  Google Scholar 

  39. Compas BE, Jaser SS, Dunbar JP, Watson KH, Bettis AH, Gruhn MA, et al. Coping and emotion regulation from childhood to early adulthood: Points of convergence and divergence. Aust J Psychol. 2014;66(2):71–81.

    Article  PubMed  Google Scholar 

  40. Skinner EA, Zimmer-Gembeck MJ. The development of coping. Annu Rev Psychol. 2007;58(1):119–44.

    Article  PubMed  Google Scholar 

  41. Watson DC, Sinha B. Emotion regulation, coping, and psychological symptoms. Int J Stress Manag. 2008;15(3):222.

    Article  Google Scholar 

  42. Zalewski M, Lengua LJ, Wilson AC, Trancik A, Bazinet A. Associations of coping and appraisal styles with emotion regulation during preadolescence. J Exp Child Psychol. 2011;110(2):141–58.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Fitzpatrick S, Dixon-Gordon KL, Turner CJ, Chen SX, Chapman A. Emotion dysregulation in personality disorders. Curr Psychiatry Rep. 2023;25(5):223–31.

    Article  PubMed  Google Scholar 

  44. Chronister J, Chou CC, Liao HY. The role of stigma coping and social support in mediating the effect of societal stigma on internalized stigma, mental health recovery, and quality of life among people with serious mental illness. J Community Psychol. 2013;41(5):582–600.

    Article  Google Scholar 

  45. Livingston JD, Boyd JE. Correlates and consequences of internalized stigma for people living with mental illness: A systematic review and meta-analysis. Soc Sci Med. 2010;71(12):2150–61.

    Article  PubMed  Google Scholar 

  46. Geffner N, Zalazar V, Mascayano F, Agrest M. Review of anti-stigma and pro-recovery programs. Acta Psiquiatr Psicol Am Lat. 2017;63(3):189–202.

    Google Scholar 

  47. Liu Y, Jiang TT, Shi TY. The Relationship Among Rumination, Coping Strategies, and Subjective Well-being in Chinese Patients With Breast Cancer: A Cross-sectional study. Asian Nurs Res. 2020;14(4):206–11.

    Article  Google Scholar 

  48. Benuto LT, Yang Y, Bennett N, Lancaster C. Distress tolerance and emotion regulation as potential mediators between secondary traumatic stress and maladaptive coping. J Interpers Violence. 2022;37(13–14):NP11557–81.

    Article  PubMed  Google Scholar 

  49. International Monetary Fund. Policy responses to COVID-19. Available from: https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19#H.

  50. Smith DJ. COVID-19 mortality and vaccine coverage—Hong Kong special administrative region, China, January 6, 2022–March 21, 2022. MMWR Morb Mortal Wkly Rep. 2022;71(15):545-8.

  51. Centre for Health Protection of the Department of Health; and the Hospital Authority. Statistics on 5th Wave of COVID-19 Hong Kong 2023. updated July 27, 2023. Available from: https://www.coronavirus.gov.hk/pdf/5th_wave_statistics/5th_wave_statistics_20230727.pdf.

  52. Authority CfHPotDoHatH. Statistics on 5th Wave of COVID-19 2023 [updated 29 Jan 2023; cited 2024 Dec 13]. Available from: https://www.coronavirus.gov.hk/pdf/5th_wave_statistics/5th_wave_statistics_20230129.pdf.

  53. Sil A, Das NK. Informed Consent Process: Foundation of the Researcher-participant Bond. Indian J Dermatol. 2017;62(4):380.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Ownby RL, Amarilis A, Kenneth G, Joshua C, Drenna W-V. Health literacy predicts participant understanding of orally-presented informed consent information. Clin Res Trials. 2015;1(1):15–9.

    PubMed  PubMed Central  Google Scholar 

  55. Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med. 1991;32(6):705–14.

    Article  CAS  PubMed  Google Scholar 

  56. Blasingame M, Mallett V, Cook M, Im W, Wilus D, Kimbrough R, et al. Association of psychosocial factors on COVID-19 testing among YWCA service recipients. Int J Environ Res Public Health. 2023;20(2):1297.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Park S-M, Cho S-i, Moon S-S. Factors associated with suicidal ideation: role of emotional and instrumental support. J Psychosom Res. 2010;69(4):389–97.

    Article  PubMed  Google Scholar 

  58. Tawfik AM, Hayek N, Mahdy SSE, Elsherbiny NM, Salem MR, Taie AFH. Social and self-stigma during COVID-19 pandemic: Egyptians’ perspectives. PLoS ONE. 2023;18(4):e0284582.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Gurvich C, Thomas N, Thomas EHX, Hudaib AR, Sood L, Fabiatos K, et al. Coping styles and mental health in response to societal changes during the COVID-19 pandemic. Int J Soc Psychiatry. 2021;67(5):540–9.

    Article  PubMed  Google Scholar 

  60. Agha S. Mental well-being and association of the four factors coping structure model: A perspective of people living in lockdown during COVID-19. Ethics Med Public Health. 2021;16:100605.

  61. Muñoz-Navarro R, Malonda E, Llorca-Mestre A, Cano-Vindel A, Fernández-Berrocal P. Worry about COVID-19 contagion and general anxiety: Moderation and mediation effects of cognitive emotion regulation. J Psychiatr Res. 2021;137:311–8.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Lee HJ, Sin C, Kim H, Im H-S, Jo J-C, Lee YJ, et al. Catastrophizing maladaptive coping affects the association between viral anxiety and fear of progression in cancer patients during COVID-19 pandemic. Psychiatry Investig. 2023;20(12):1204.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Muthén BO. Goodness of fit with categorical and other nonnormal variables. SAGE Focus Editions. 1993;154:205.

    Google Scholar 

  64. Garnefski N, Kraaij V. Cognitive emotion regulation questionnaire–development of a short 18-item version (CERQ-short). Personality Individ Differ. 2006;41(6):1045–53.

    Article  Google Scholar 

  65. Hu Lt, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1–55.

    Article  Google Scholar 

  66. Wang Q, Yang L, Jin H, Lin L. Vaccination against COVID-19: A systematic review and meta-analysis of acceptability and its predictors. Prev Med. 2021;150:106694.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Wang J, Zhu H, Lai X, Zhang H, Huang Y, Feng H, et al. From COVID-19 vaccination intention to actual vaccine uptake: A longitudinal study among Chinese adults after six months of a national vaccination campaign. Expert Rev Vaccines. 2022;21(3):385–95.

    Article  CAS  PubMed  Google Scholar 

  68. Paul E, Steptoe A, Fancourt D. Attitudes towards vaccines and intention to vaccinate against COVID-19: Implications for public health communications. Lancet Reg Health Eur. 2021;1:100012.

    Article  PubMed  Google Scholar 

  69. Cordina M, Lauri MA, Lauri J. Attitudes towards COVID-19 vaccination, vaccine hesitancy and intention to take the vaccine. Pharm Pract. 2021;19(1):2317.

    Google Scholar 

  70. Detoc M, Bruel S, Frappe P, Tardy B, Gagneux-Brunon A. Intention to participate in a COVID-19 vaccine clinical trial and to get vaccinated against COVID-19 in France during the pandemic. Vaccine. 2020;38(45):7002–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Al-Mohaithef M, Padhi BK. Determinants of COVID-19 Vaccine Acceptance in Saudi Arabia: A Web-Based National Survey. J Multidiscip Healthc. 2020;13:1657–63.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Žeželj I, Petrović M, Ivanović A, Kurčubić P. I trust my immunity more than your vaccines:“Appeal to nature” bias strongly predicts questionable health behaviors in the COVID-19 pandemic. PLoS ONE. 2023;18(2):e0279122.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Zar HJ, MacGinty R, Workman L, Botha M, Johnson M, Hunt A, et al. Natural and hybrid immunity following four COVID-19 waves: A prospective cohort study of mothers in South Africa. EClinicalMedicine. 2022;53:101655.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Liao Q, Fielding R, Cheung YTD, Lian J, Yuan J, Lam WWT. Effectiveness and parental acceptability of social networking interventions for promoting seasonal influenza vaccination among young children: randomized controlled trial. J Med Internet Res. 2020;22(2):e16427.

    PubMed  PubMed Central  Google Scholar 

  75. Yanos PT, Lucksted A, Drapalski AL, Roe D, Lysaker P. Interventions targeting mental health self-stigma: A review and comparison. Psychiatr Rehabil J. 2015;38(2):171.

    Article  PubMed  Google Scholar 

  76. Bekki JM, Smith ML, Bernstein BL, Harrison C. Effects of an online personal resilience training program for women in STEM doctoral programs. J Women Minorities Sci Eng. 2013;19(1):17–35.

    Article  Google Scholar 

  77. Sahler OJZ, Dolgin MJ, Phipps S, Fairclough DL, Askins MA, Katz ER, et al. Specificity of problem-solving skills training in mothers of children newly diagnosed with cancer: Results of a multisite randomized clinical trial. J Clin Oncol. 2013;31(10):1329.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Vahdani S, Rezaei M, Alimahdi M, Shariat Bagheri MM. Predicting Corona Vaccine Acceptance Based on Corona Anxiety, Optimism and Rumination in Medical Sciences Students. J Sch Public Health Inst Public Health Res. 2023;20(4):379–92.

    Google Scholar 

  79. Hartley CA, Phelps EA. Changing fear: the neurocircuitry of emotion regulation. Neuropsychopharmacology. 2010;35(1):136–46.

    Article  PubMed  Google Scholar 

  80. Ruiter RA, Kessels LT, Peters GJY, Kok G. Sixty years of fear appeal research: Current state of the evidence. Int J Psychol. 2014;49(2):63–70.

    Article  PubMed  Google Scholar 

  81. Diaconescu LV, Gheorghe IR, Cheşcheş T, Popa-Velea O. Psychological variables associated with HPV vaccination intent in Romanian academic settings. Int J Environ Res Public Health. 2021;18(17):8938.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Taha SA, Matheson K, Anisman H. The 2009 H1N1 influenza pandemic: The role of threat, coping, and media trust on vaccination intentions in Canada. J Health Commun. 2013;18(3):278–90.

    Article  PubMed  Google Scholar 

  83. Hensher M. Covid-19, unemployment, and health: time for deeper solutions? BMJ. 2020;371:m3687.

    Article  PubMed  Google Scholar 

  84. Crawford AM. Stigma Associated With AIDS: A Meta-Analysis1. J Appl Soc Psychol. 2010;26(5):398–416.

    Article  Google Scholar 

  85. Pantelic M, Steinert JI, Park J, Mellors S, Murau F. “Management of a spoiled identity”: systematic review of interventions to address self-stigma among people living with and affected by HIV. BMJ Glob Health. 2019;4(2):e001285.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Goulia P, Mantas C, Dimitroula D, Mantis D, Hyphantis T. General hospital staff worries, perceived sufficiency of information and associated psychological distress during the A/H1N1 influenza pandemic. BMC Infect Dis. 2010;10:1–11.

    Article  Google Scholar 

  87. Stringer KL, Mukherjee T, McCrimmon T, Terlikbayeva A, Primbetovac S, Darisheva M, Hunt T, Gilbert L, El-Bassel N. Attitudes towards people living with HIV and people who inject drugs: A mixed method study of stigmas within harm reduction programs in Kazakhstan. Int J Drug Policy. 2019;68:27–36.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Yu Y, Yen SHY, Jiang LC, Ming W-k, Lau MM, Lau JT. Levels and Determinants of COVID-19 Vaccination at a Later Phase among Chinese Older People Aged 60 Years or Older: A Population-Based Survey. Vaccines. 2023;11(6):1029.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Huynh G, Tran TT, Nguyen TN, Pham LA. COVID-19 vaccination intention among healthcare workers in Vietnam. Asian Pac J Trop Med. 2021;14(4):159–64.

    Article  CAS  Google Scholar 

  90. Zampetakis LA, Melas C. The health belief model predicts vaccination intentions against COVID-19: A survey experiment approach. Appl Psychol Health Well Being. 2021;13(2):469–84.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Nunnally JC. An Overview of Psychological Measurement. In: Wolman BB, editor. Clinical Diagnosis of Mental Disorders. Boston: Springer; 1978. p.97–146.

  92. Cohen J. A power primer. In: Kazdin AE, editor. Methodological issues and strategies in clinical research. 4th ed. 2016. p. 279–84.

  93. MacKinnon D. Introduction to statistical mediation analysis. New York: Routledge; 2012.

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Acknowledgements

We would like to thank participants for their contribution to the present study.

Funding

This research was funded by internal funding of the Center for Health Behaviors Research, the Chinese University of Hong Kong.

Author information

Author notes

  1. Xiaoying Zhang and Yiming Luan contributed equally to this work.

Authors and Affiliations

  1. College of Public Health, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China

    Xiaoying Zhang

  2. Meilong Community Health Center of Minhang District, Shanghai, 201100, China

    Yiming Luan

  3. School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China

    Yihan Tang & Jing Gu

  4. Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China

    Mason M. C. Lau

  5. School of Public Health, Fudan University, Shanghai, 200032, China

    Yanqiu Yu

  6. Public Mental Health Center, School of Mental Health, Wenzhou Medical University, Wenzhou, 325000, China

    Joseph T. F. Lau

  7. Zhejiang Provincial Clinical Research Center for Mental Disorders, The Affiliated Wenzhou Kangning Hospital, Wenzhou Medical University, Wenzhou, 325000, China

    Joseph T. F. Lau

  8. Center for Health Behaviors Research, The Chinese University of Hong Kong, Hong Kong, China

    Joseph T. F. Lau

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  4. Mason M. C. Lau
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  5. Yanqiu Yu
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  6. Jing Gu
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Contributions

X.Z. contributed to data analysis, writing up and finalizing the manuscript. J.T.-F.L. contributed to conceptualization, methodology, validation, resources, data curation, review & editing, supervision, project administration, and funding acquisition. Y.Y. contributed to study design, questionnaire design, conceptualization, methodology, software, review & editing. M.M.C.L conducted the investigation. Y.L. contributed to design, implementation, and writing of the manuscript. Y.T. contributed to literature review, filed work coordination. J.G. contributed to conceptual framework, study design, questionnaire design, and review of manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yanqiu Yu, Jing Gu or Joseph T. F. Lau.

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Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki. The study was approved by the Survey and Behavioral Research Ethics Committee of the Chinese University of Hong Kong (No. SBRE-21–0778). Informed consent was obtained from all subjects involved in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Zhang, X., Luan, Y., Tang, Y. et al. Perceived social support and self-stigma as factors of COVID-19 booster vaccination behavior and intention via cognitive coping and emotion regulation among people infected with COVID-19 in Hong Kong. BMC Public Health 25, 659 (2025). https://doi.org/10.1186/s12889-025-21899-x

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Keywords

BMC Public Health

ISSN: 1471-2458

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