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Psychosocial correlates of parents’ willingness to vaccinate their children against COVID-19

  • Hyunmin Yu ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

    hyuy@nursing.upenn.edu

    Affiliation School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

  • Stephen Bonett,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Writing – review & editing

    Affiliation School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

  • Ufuoma Oyiborhoro,

    Roles Conceptualization, Writing – review & editing

    Affiliation School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

  • Subhash Aryal,

    Roles Conceptualization, Methodology, Supervision, Writing – review & editing

    Affiliation School of Nursing, Johns Hopkins University, Baltimore, Maryland, United States of America

  • Andrew Kim,

    Roles Conceptualization, Writing – review & editing

    Affiliation School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

  • Melanie L. Kornides,

    Roles Writing – review & editing

    Affiliation School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

  • John B. Jemmott,

    Roles Formal analysis, Investigation, Writing – review & editing

    Affiliation Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

  • Karen Glanz,

    Roles Writing – review & editing

    Affiliations School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

  • Antonia M. Villarruel,

    Roles Formal analysis, Funding acquisition, Investigation, Writing – review & editing

    Affiliation School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

  • José A. Bauermeister

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Writing – review & editing

    Affiliation School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

Abstract

Background

Public health guidance recommended that children who are 6 months or older be vaccinated against COVID-19 in June of 2022. In the U.S., 56% of children under 17 had not received the COVID-19 vaccination in 2023. We examine parents’ willingness to vaccinate their children against COVID-19 using the theory of planned behavior in order to design effective strategies to promote vaccine uptake.

Methods

The Philadelphia Community Engagement Alliance is part of an NIH community-engaged consortium focused on addressing COVID-19 disparities across the U.S. We surveyed 1,008 Philadelphia parents (mean age 36.86, SD 6.55; 42.3% racial/ethnic minorities) between September 2021 and February 2022, a period when guidance for child vaccination was anticipated. Structural Equation Modeling analysis examined associations between parental willingness and vaccine-related attitudes, norms, and perceived control. Covariates included parents’ COVID-19 vaccination status, race/ethnicity, gender, and survey completion post-CDC pediatric COVID-19 vaccination guidelines. Subgroup analyses by race/ethnicity and gender were conducted.

Results

Our model demonstrated good fit (χ2 = 907.37, df = 419, p<0.001; comparative fit index [CFI] = 0.951; non-normed fit index [NNFI] = 0.946; root mean square error of approximation [RMSEA] = 0.034 with 95% CI = 0.030–0.038). Attitudes ( = 0.447, p<0.001) and subjective norms ( = 0.309, p = 0.002) were predictors of intention. Racial/ethnic minority parents exhibited weaker vaccination intentions ( = -0.053, p = 0.028) than non-Hispanic White parents.

Conclusions

Parents’ attitudes and norms influence their vaccination intentions. Despite the survey predating widespread child vaccine availability, findings are pertinent given the need to increase and sustain pediatric vaccinations against COVID-19. Interventions promoting positive vaccine attitudes and prosocial norms are warranted. Tailored interventions and diverse communication strategies for parental subgroups may be useful to ensure comprehensive and effective vaccination initiatives.

Introduction

The novel coronavirus disease 2019 (COVID-19) pandemic resulted in substantial human loss, exacerbated socio-economic inequities, and highlighted health system disparities, impacting communities globally [13]. Since the onset of the COVID-19 pandemic, public health authorities world-wide instituted an array of wide-scale measures to prevent the spread of the virus, including social distancing, hand hygiene, mask-wearing, travel restrictions, and quarantine protocols [4]. Central to these containment efforts was vaccination [5, 6]. After the World Health Organization (WHO) urged the pursuit of COVID-19 vaccine research and development, remarkable global vaccine research efforts led to the expeditious development and approval of several COVID-19 vaccines, including those developed by Moderna, Pfizer/BioNTech, and AstraZeneca [7].

Despite these significant strides and the vital role of vaccination in promoting wellbeing and quality of life during the COVID-19 pandemic [8, 9], vaccine hesitancy and refusal emerged as widespread challenges, posing barriers to achieving herd immunity [1012] and influencing decision-making processes [13, 14]. Notably, in the U.S., as of May 2023, 56% of children under the age of 17 had not yet received the COVID-19 vaccination [15]. While studies have examined various factors associated with parental intentions to vaccinate their children against COVID-19, including social norms, sociodemographic characteristics, and vaccination-related attitudes [16, 17], understanding the theory-informed factors that influence parents’ intentions regarding the COVID-19 vaccine for their children is crucial in ensuring widespread vaccine acceptance and optimal protection against infection and severe disease.

The Theory of Planned Behavior (TPB) [18, 19] provides a robust framework to examine and predict human behavior, particularly in health-related decision-making. TPB posits and research confirms [20, 21] that behavioral intentions strongly predict implemented behavior, especially in the context of reasoned and goal-directed actions. According to the TPB, behavioral intentions are influenced by three key constructs: (1) the individual’s favorable or unfavorable perception and evaluation of a particular behavior (attitudes toward behavior), (2) perceptions of social approval to perform or not perform the behavior (subjective norms), and (3) the perception of the ease or difficulty of executing the behavior (perceived behavioral control). These factors collectively shape an individual’s intention to engage in a specific behavior, and thus are important determinants of actual behavior [19].

The TPB has demonstrated its effectiveness as a reliable predictor of both COVID-19 vaccine intentions and COVID-19 vaccination, as evidenced by multiple studies conducted across various countries and populations [22]. Some studies applied the TPB to investigate people’s intentions to receive future hypothetical vaccines prior to the release of the COVID-19 vaccines [2326]. After the COVID-19 vaccines became available, additional studies have investigated the relationship between TPB and people’s intentions to receive these vaccines [2731].

Table 1 summarizes findings from prior US-based studies that utilized the TPB to investigate intentions to vaccinate against COVID-19. All centered around adult populations, with one emphasizing older adults aged 60 or above [32], and another highlighting young adults, specifically college students [26]. Only one of the eight studies we identified reported that all three core TPB constructs (attitudes, subjective norms, and perceived behavioral control) were predictors of COVID-19 vaccination intention [33]. Four studies found that attitudes and subjective norms were significant predictors of COVID-19 vaccine intention, while perceived behavioral control was not significantly associated with intention [23, 32, 34, 35]. Another study [36] found attitudes and perceived behavioral control to be predictors and subjective norms to be unrelated. A different study [37] indicated that attitudes and subjective norms were predictive, but it did not include perceived behavioral control in the analyses. A study focusing on college students [26] reported that only subjective norms were found to be significant, while attitudes and perceived behavioral control were not.

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Table 1. US-based studies applying the TPB to examine COVID-19 vaccination intention.

https://doi.org/10.1371/journal.pone.0305877.t001

The varied findings among these studies highlight the complexity of factors influencing vaccination intention and underscore the importance of understanding these factors in different populations and contexts. No previous studies have proposed a TPB-based model to examine parents’ intention to have their children vaccinated. Addressing this research gap is crucial for gaining a comprehensive and essential understanding of the dynamics behind family vaccination decision-making.

Conducting a study that examines parents’ intention to have their children vaccinated is greatly significant for several reasons. First, a TPB-informed model would enable researchers to understand the factors that influence COVID-19 vaccination decision-making within families through the exploration of parental attitudes. Second, understanding the intentions of parents can inform the design of communication campaigns, educational initiatives, and interventions aimed at promoting vaccine acceptance and improving vaccination rates across various age groups. Moreover, parental attitudes and decisions profoundly impact children’s healthcare choices and outcomes [38, 39]. Therefore, comprehending parents’ intentions is essential in shaping vaccination among the younger population.

Our research aims to use TPB as the foundational framework to identify the underlying factors influencing parents’ intentions to have their child receive the COVID-19 vaccine, including attitudes, perceived behavioral control, and subjective norms, and to understand how these constructs influence their ultimate decision-making.

Our hypotheses are:

  1. Consistent with the TPB, we hypothesized that there is a direct positive association between COVID-19 vaccination intention and the following constructs within parent populations: (a) attitudes toward COVID-19 vaccination; (b) subjective norms; and (c) perceived behavioral control (Fig 1). The hypothesized TPB model (Fig 1) includes four latent variables: (1) intention, (2) attitudes, (3) perceived behavioral control (PBC), and (4) subjective norms (SN) and four observed variables: parental vaccination status, survey completion dates, race/ethnicity and gender.
  2. Given reported differences in vaccine-related attitudes and uptake by race/ethnicity [4042], we hypothesized that there is a difference in these associations between non-Hispanic White parents and parents from racial and ethnic minority backgrounds.
  3. Give emergent data regarding differences by parental gender in COVID-19 vaccination [4345], we further hypothesized that there would be a difference in these associations between male parents and female parents.
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Fig 1. Parents’ vaccination intentions as informed by the theory of planned behavior.

https://doi.org/10.1371/journal.pone.0305877.g001

Methods

Procedure

This cross-sectional study is a part of the broader Philadelphia CEAL (Community Engagement Alliance) study, which aimed to mitigate disparities in COVID-19 testing, vaccine uptake, and clinical trial participation among communities disproportionately affected by the pandemic in the city of Philadelphia. Initially, 8,153 people were recruited via online and community-based outreach to participate in a baseline self-administered screener and confidential survey from September 7th 2021 to February 14th 2022. Eligibility to participate in the survey required participants to reside in Philadelphia and be at least 13 years of age. A total of 3,936 participants were initially deemed valid and eligible through a real-time fraud detection protocol developed for this study. After an additional post-hoc fraud detection process, the number of valid participants was further narrowed down to 2,870. Detailed information regarding the fraud detection process used for this study has been published elsewhere [4648].

For this study, we extracted the subset of participants from the original dataset that identified as parents. The parent sample consisted of 1,309 participants, each of whom had at least one child ≤ 17, irrespective of the participant’s age. All study procedures were approved by the University of Pennsylvania Institutional Review Board (#848650), and written informed consents were acquired from all participants.

Measures

TPB constructs.

The questionnaire about COVID-19 vaccines for parent participants were developed based on the TPB [19]. It encompassed the original constructs of the TPB, which consisted of (1) intention, (2) attitudes, (3) perceived behavioral control, and (4) subjective norms. Multiple items were utilized to measure each TPB construct. For the questionnaire, positively coded items were employed, encompassing 9 attitudes items, 7 subjective norms items, and 9 perceived behavioral control items. The wording of these items across the three constructs can be found in Table 2. All responses were recorded on a five-point Likert scale, ranging from ‘1 = very unlikely’ to ‘5 = very likely.’ Higher scores for each construct indicated more positive attitudes, greater degree of positive subjective norms, and higher perceived behavioral, respectively, among the participants.

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Table 2. The Theory of Planned Behavior (TPB) measurement model.

https://doi.org/10.1371/journal.pone.0305877.t002

Vaccination intent.

The intention of parent participants to have their child vaccinated was evaluated using a single item: “I am willing to have my child receive a COVID-19 vaccine.” Parents provided their answers on a scale ranging from ‘1 = strongly disagree’ to ‘5 = strongly agree,’ with additional options for ‘2 = somewhat disagree,’ ‘3 = neither agree nor disagree,’ and ‘4 = somewhat agree.’

Parent vaccination history.

Parents’ prior COVID-19 vaccination status was measured through one item: ‘Have you received at least one dose of the COVID-19 vaccine?’ Participants selected from several response options, including ‘1 = yes, got one-dose vaccine,’ ‘2 = yes, got first dose of two-dose vaccine,’ ‘3 = yes, got both doses of a two-dose vaccine,’ ‘4 = no, have not received the vaccine,’ ‘5 = don’t know,’ ‘6 = prefer not to answer,’ and ‘7 = yes, got more than two doses of a vaccine (e.g., an additional dose or booster).’ The responses were modified into five categories to enhance clarity, merging ‘1’ and ‘3’ as ‘yes, got full primary series,’ and combining ‘5’ and ‘6’ as ‘don’t know/prefer not to answer.’

Pediatric vaccine recommendation.

Taking into account the Centers for Disease Control and Prevention’s (CDC) initial guideline, which recommended pediatric COVID-19 vaccines for children on November 2, 2021 [49], the completion dates of parents’ surveys were dichotomized into ‘0 = on or before 11/2/2021, before recommendation’ and ‘1 = after 11/2/2021, after recommendation.’

Statistical analysis

We conducted descriptive statistical analyses, encompassing frequency distributions and means, using R version 4.2.3. To examine the hypothesized TPB model, we employed Structural Equation Modeling (SEM) through the Lavaan [50, 51]. We employed a two-stage modeling approach [52]. In the initial stage, Confirmatory Factor Analysis (CFA) was conducted to test the factorial validity of the latent constructs and the adequacy of the measurement model. The three latent constructs, namely attitudes, perceived behavioral control, and subjective norms, were included in this stage. The standardized correlation coefficients were assessed to determine if they fell below the cut-off point of 0.85 [53]. To identify potential sources of misfit, modification indices were examined, providing a foundation for re-specifying the measurement model as needed.

After specifying the measurement model, the next step involved performing SEM to assess whether the hypothesized TPB model demonstrated acceptable fit to the data and to estimate the effects of relationships within the model. The SEM model comprised three latent variables, with parental vaccination status, binary survey completion dates before and after the CDC’s recommendation for the pediatric vaccine, gender, race and ethnicity included as observed variables. Several statistical parameters were employed to evaluate the model’s fit to the data, including the chi-square (χ2) test, comparative fit index (CFI), non-normed fit index (NNFI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) [54]. A statistically non-significant result for the chi-square test (p > 0.05) would indicate a good fit for the model. However, it is noted that the chi-square test’s significance is highly influenced by sample size, large samples can lead to significant p-values even with minor misspecifications [55]. With this, focus was placed on the remaining fit indices. In accordance with established guidelines [54, 56], good fit is indicated by CFI and NNFI values exceeding 0.90 or 0.95, an RMSEA value below 0.06, and an SRMR value under 0.05. These fit indices served as key indicators to evaluate the adequacy of the hypothesized TPB model with the collected data.

After establishing the SEM, we conducted a subgroup analysis to compare a SEM model for parents from racial and ethnic minority backgrounds with a model for non-Hispanic White parents. Additionally, we compared a SEM model for cisgender male parents with a model for cisgender female parents. These analyses were performed based upon previous research evidence indicating differences in vaccination intentions based on race, ethnicity, and gender [5759].

We excluded 301 participants who did not respond to any survey items from the initial 1,309, resulting in 1,008 participants included in the CFA and SEM analyses. For participants with partially missing survey data, we utilized multiple imputations by chained equations [60], and the imputed datasets using Rubin’s rules [61] with a common set of m = 5 imputations. This approach facilitated data retention in multivariable models, enhancing the robustness and reliability of the analysis. After completing the imputation process, a binary indicator variable was generated, denoting ‘0 = not imputed/no missing’ and ‘1 = imputed.’ This variable was subsequently employed as a control variable in the SEM analysis. Its inclusion as a control variable allowed for the examination and potential adjustment of any imputation-related effects on the SEM results, contributing to the overall rigor and validity of the study’s findings. We additionally conducted a sensitivity analysis, affirming the consistency of results between our observed data and imputed data for our overall structural model. Last, we enhanced our model by introducing bootstrap standard errors with 1,000 replicates. This technique adds an additional layer of reliability to our analysis by accounting for potential sampling variability.

Results

Our final study sample consisted of 1008 Philadelphia parents (Table 3). Their average age of was 36.86 (SD 6.55). About 57.7% of the parent participants identified as non-Hispanic White, 65.1% identified as women, and 92.8% identified as heterosexual. Parents who received more than one COVID-19 vaccine accounted for 97.2%, while those who did not receive any COVID-19 vaccine constituted 2.4%, and 0.4% chose not to share their vaccination status. Participants who completed surveys on or before the date the vaccine was recommended by the CDC for children (11/2/2021) versus those who completed them after did not exhibit statistically significant differences in their attitudes (M 4.11, SD 1.00; M 3.92, SD 0.68; p = 0.419), subjective norms (M 4.22, SD 0.85; M 3.94, SD 0.58; p = 0.197), perceived behavioral control (M 4.24, SD 0.85; M 3.89, SD 0.68; p = 0.087) and intention (M 4.00, SD 1.55; M 3.86, SD 1.04; p = 0.679).

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Table 3. Demographic characteristics for the participating parents.

https://doi.org/10.1371/journal.pone.0305877.t003

Measurement model

In the full measurement model for parents the standardized factor loadings ranged from 0.461 to 0.689 and were all significant (p < 0.001; Table 2). The standardized correlation coefficients ranged from 0.768 to 0.864 (S1 Appendix). All factor correlations with one exception remained below the cut-off point of 0.85. These findings collectively underscore the model’s overall satisfactory level of discriminant validity across the latent constructs.

The hypothesized three-factor measurement model showed a good fit, as evidenced by the fit indices (χ2 = 544.231; df = 272, p < 0.001; CFI = 0.970; NNFI = 0.967; RMSEA = 0.032 with 95% CI = 0.027–0.037; SRMR = 0.029). Post-hoc modifications were unnecessary due to the good fit of the data to the model.

Structural model

The full structural model exhibited a good fit (χ2 = 907.370; df = 419, p < 0.001; CFI = 0.951; NNFI = 0.946; RMSEA = 0.034 with 95% CI = 0.030–0.038; SRMR = 0.050). Among the constructs, attitudes displayed the strongest predictive power on intention (standardized = 0.447, p < 0.001), followed by subjective norms (standardized = 0.309, p = 0.002) (Table 4). The effect of perceived behavioral control on intention was negative, albeit lacking statistical significance at the level of 0.05 (standardized = -0.086, p = 0.171). Among observed variables, race and ethnicity were statistically significantly associated with intention (standardized = -0.053, p = 0.028, indicating parents from racial/ethnic minority backgrounds expressed a weaker intention to vaccinate their children than non-Hispanic White parents. Parents’ vaccination status (standardized = -0.010, p = 0.727), the survey completion dates (standardized = 0.023, p = 0.361) and gender (standardized = -0.017, p = 0.486) were not significantly associated with intention (Fig 2).

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Fig 2. The overall structural equation model.

* = p < 0.05, ** = p < 0.01, *** = p < 0.001.

https://doi.org/10.1371/journal.pone.0305877.g002

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Table 4. Estimated regression coefficients for the structural equation model.

https://doi.org/10.1371/journal.pone.0305877.t004

Subgroup analyses

The hypothesized effects exhibited variations across racial and ethnic subgroups (Table 4). Among parents from racial and ethnic minority backgrounds, attitudes (standardized = 0.530, p < 0.001) and subjective norms (standardized = 0.299, p = 0.040) had a significant impact on intention to have their children vaccinated (Fig 3). However, among non-Hispanic White parents, subjective norms alone were found to have a significant influence on intention (standardized = 0.318, p = 0.031), while attitudes did not demonstrate a statistically significant effect (standardized = 0.283, p = 0.100) (Fig 4). Furthermore, the effects varied across cisgender subgroups. Among male parents, none of the TPB constructs were significantly associated with intention (Fig 5). However, among female parents, attitudes (standardized = 0.455, p < 0.001) and subjective norms (standardized = 0.331, p = 0.006) had a significant impact on intention (Fig 6).

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Fig 3. The structural equation model for racial and ethnic minority parents.

* = p < 0.05, ** = p < 0.01, *** = p < 0.001.

https://doi.org/10.1371/journal.pone.0305877.g003

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Fig 4. The structural equation model for Non-Hispanic White parents.

* = p < 0.05, ** = p < 0.01, *** = p < 0.001.

https://doi.org/10.1371/journal.pone.0305877.g004

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Fig 5. The structural equation model for male parents.

* = p < 0.05, ** = p < 0.01, *** = p < 0.001.

https://doi.org/10.1371/journal.pone.0305877.g005

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Fig 6. The structural equation model for female parents.

* = p < 0.05, ** = p < 0.01, *** = p < 0.001.

https://doi.org/10.1371/journal.pone.0305877.g006

Discussion

In this study we used TPB, a well-established theoretical framework, to predict parents’ intention to vaccinate their children against COVID-19. The theory holds that an attitude toward the behavior, the subjective norms regarding the behavior, and perceived behavioral control have direct and independent effects on intention. Structural equation modeling revealed that attitudes and subjective norms predicted vaccine intention, consistent with TPB. Parents who favorably evaluated vaccinating their children and those who perceived support for vaccinating the children among their important referents expressed a greater willingness to vaccinate their children against COVID-19. However, contrary to the theory, perceived behavioral control, beliefs about how easy or hard it would be to vaccinate their children against COVID-19, was unrelated to their intention.

A basic tenet of TPB is that the relative predictive power of attitudes, subjective norms, and perceived behavioral control varies depending on the behavior and the population in question [19]. Our findings suggest that attitudes and subjective norms may be more influential than perceived behavioral control on the intention of parents to vaccinate their children against COVID-19. The inability of perceived behavioral control to predict intention may mean that the parents in our population did not differ much in whether they had the necessary resources and opportunities to acquire the vaccine for their children. Although this explanation is only conjecture, our finding that perceived behavioral control is unrelated to vaccine intention has been observed in other studies. Indeed, most studies have found that attitudes and subjective norms affect vaccine intentions more than perceived behavioral control [23, 32, 34, 35]. This study is the first to investigate parents’ intentions to vaccinate their children against COVID-19. It suggests that the previously observed relationship in studies focused on adult populations, where perceived behavioral control is unrelated to vaccine intentions for adults themselves, also applies to parents’ intentions regarding COVID-19 vaccinations for their children.

Our study further explored these intentions within the contexts of racial and ethnic backgrounds and gender, shedding light on how these factors significantly influence vaccination-related decision-making. Notably, our analysis revealed substantial variations in the effects of key TPB constructs across different racial and ethnic subgroups. Among parents from racial and ethnic minority backgrounds, attitudes and subjective norms exhibited significant positive associations with the intention to have their children vaccinated. These findings align with prior research underscoring the importance of attitudes and social norms in shaping vaccination behavior [58, 59, 62]. The robust relationship between positive attitudes and vaccination intention highlights the significance of individuals’ overall favorable perceptions of vaccination for their children. Additionally, the role of subjective norms underscores the influence of social networks and cultural contexts on vaccination decisions. In contrast, for non-Hispanic White parents, social norms appeared to play a more prominent role in shaping their intentions to vaccinate their children. The significance of subjective norms in this subgroup emphasizes the importance of understanding community-level influences and peer interactions in vaccination decision-making processes [63, 64].

Furthermore, our findings demonstrated variations across cisgender subgroups. While previous studies have reported higher vaccination intentions among males compared to females [57, 58, 65], these findings were typically related to self-vaccination rather than vaccination decisions for their children. Among male parents in our study, none of the TPB constructs displayed a statistically significant association with vaccination intention. This suggests that factors beyond the scope of TPB measurements may be influencing vaccination decisions in this subgroup. In contrast, among female parents, attitudes and subjective norms emerged as significant predictors of vaccination intention, aligning with the overall findings.

The attitudes and subjective norms associated with intentions in the present study are modifiable. Decisions about which variables to target with an intervention should be based on empirical evidence rather than broad generalizations. Thus, for those who would intervene to increase parents’ intention to vaccinate their children against COVID-19, the present results suggest that strategies should be utilized to instill positive attitudes among parents toward vaccinating their children and supportive normative beliefs regarding their significant referents. In this connection, strategies need to be developed to increase positive evaluations of vaccination for children. Social network interventions may also be an effective strategy to change parents’ subjective norms about vaccinating their children.

Several factors must be weighed when evaluating this study. A strength of the study is the use of TPB, a theory-based approach. Another strength is the large sample, including many minoritized individuals. However, the use of a convenience sample and substantial complete missingness for the TPB items may introduce selection bias, limiting the generalizability of the findings to all parents. Another potential limitation is the self-reported data, which may introduce response bias and social desirability bias. Participants may have provided responses that they perceived as socially acceptable or in line with societal norms, potentially affecting the accuracy of their reported attitudes and intentions regarding vaccination. Nonetheless, our study provides insight and context for the behavioral factors influencing parents’ intentions to have their children vaccinated among a sample of Philadelphia parents. Future research could delve deeper into the mechanisms underlying variations among different parental subpopulations and explore additional factors that may contribute to vaccination decision-making within specific subgroups. Moreover, ongoing efforts to address vaccine hesitancy and enhance vaccination rates should consider tailoring strategies to the unique characteristics and influences within these subpopulations.

Conclusions

In conclusion, our study contributes to the growing body of literature on vaccination behavior by highlighting the differential influences of attitudes, subjective norms, and intentions regarding parents vaccinating their children across racial, ethnic, and gender subgroups. These findings underscore the need for targeted interventions and communication strategies that consider the unique factors shaping vaccination intentions among diverse populations.

There is a great need for theory-driven interventions to increase COVID-19 vaccine uptake, including interventions to encourage U.S. parents to vaccinate their children. Efforts to understand and change intentions in this population might be most successful if they focus on attitudes and subject norms, as the present research has highlighted. Future research must clarify why perceived behavioral control is unrelated to intention. Research along these lines would contribute to efforts to reduce the children’s risk of COVID-19 infection and, more generally, help curb the COVID-19 pandemic.

Supporting information

S1 Appendix. Standardized factor correlation coefficients.

https://doi.org/10.1371/journal.pone.0305877.s001

(DOCX)

Acknowledgments

We thank our participants and community partners for their participation in the National Institutes of Health Community Engagement Alliance (CEAL) imitative.

References

  1. 1. Nicola M, Alsafi Z, Sohrabi C, Kerwan A, Al-Jabir A, Iosifidis C, et al. The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International Journal of Surgery. 2020;78:185–93. pmid:32305533
  2. 2. Mishra NP, Das SS, Yadav S, Khan W, Afzal M, Alarifi A, et al. Global impacts of pre-and post-COVID-19 pandemic: Focus on socio-economic consequences. Sensors International. 2020;1:100042. pmid:34766044
  3. 3. Buheji M, da Costa Cunha K, Beka G, Mavric B, De Souza Y, da Costa Silva SS, et al. The extent of covid-19 pandemic socio-economic impact on global poverty. A global integrative multidisciplinary review. American Journal of Economics. 2020;10(4):213–24.
  4. 4. Centers for Disease Control and Prevention. Isolation and precautions for people with COVID-19. 2023. Available from: https://www.cdc.gov/coronavirus/2019-ncov/your-health/isolation.html.
  5. 5. Viana J, van Dorp CH, Nunes A, Gomes MC, van Boven M, Kretzschmar ME, et al. Controlling the pandemic during the SARS-CoV-2 vaccination rollout. Nature Communications. 2021;12(1):3674. pmid:34135335
  6. 6. Moghadas SM, Vilches TN, Zhang K, Wells CR, Shoukat A, Singer BH, et al. The impact of vaccination on COVID-19 outbreaks in the United States. medRxiv [Preprint]. 2020. pmid:33269359
  7. 7. World Health Organization. Coronavirus disease (COVID-19): Vaccine research and development. 2021. Available from: https://www.who.int/news-room/questions-and-answers/item/coronavirus-disease-(covid-19)-vaccine-research-and-development.
  8. 8. Prasetyo YB, Faridi F, Masruroh NL, Melizza N, Kurnia AD, Wardojo SSI, et al. Path analysis of the relationship between religious coping, spiritual well-being, and family resilience in dealing with the COVID-19 pandemic in Indonesia. Asian Journal of Social Health and Behavior. 2024;7(1):1–10.
  9. 9. Lin C-Y, Fan C-W, Ahorsu DK, Lin YC, Weng H-C, Griffiths MD. Associations between vaccination and quality of life among Taiwan general population: A comparison between COVID-19 vaccines and flu vaccines. Human Vaccines & Immunotherapeutics. 2022;18(5):2079344. pmid:35679589
  10. 10. Dror AA, Eisenbach N, Taiber S, Morozov NG, Mizrachi M, Zigron A, et al. Vaccine hesitancy: The next challenge in the fight against COVID-19. European Journal of Epidemiology. 2020;35:775–9. pmid:32785815
  11. 11. Machingaidze S, Wiysonge CS. Understanding COVID-19 vaccine hesitancy. Nature Medicine. 2021;27(8):1338–9. pmid:34272500
  12. 12. Khubchandani J, Sharma S, Price JH, Wiblishauser MJ, Sharma M, Webb FJ. COVID-19 vaccination hesitancy in the United States: A rapid national assessment. Journal of Community Health. 2021;46:270–7. pmid:33389421
  13. 13. Akinsola KO, Bakare AA, Gobbo E, King C, Hanson C, Falade A, et al. A systematic review of measures of healthcare workers’ vaccine confidence. Human Vaccines & Immunotherapeutics. 2024;20(1):2322796. pmid:38506574
  14. 14. Isonne C, Marzuillo C, Villari P, Baccolini V. The role of vaccine literacy and health literacy in the health prevention decision-making process. Human Vaccines & Immunotherapeutics. 2024;20(1):2321675. pmid:38445564
  15. 15. American Academy of Pediatrics. Summary of data publicly reported by the Centers for Disease Control and Prevention. 2023. Available from: https://www.aap.org/en/pages/2019-novel-coronavirus-covid-19-infections/children-and-covid-19-vaccination-trends/#:~:text=Child%20vaccination%20rates%20vary%20widely%20across%20states%2C%20ranging%20from%2017,83%25%20receiving%20their%20first%20dose.&text=About%208.3%20million%20children%2012,received%20their%20first%20vaccine%20dose.
  16. 16. Willis DE, Schootman M, Shah SK, Reece S, Selig JP, Andersen JA, et al. Parent/guardian intentions to vaccinate children against COVID-19 in the United States. Human Vaccines & Immunotherapeutics. 2022;18(5):2071078. pmid:35506876
  17. 17. Lin C-Y, Hsiao RC, Chen Y-M, Yen C-F. A parent version of the motors of COVID-19 vaccination acceptance scale for assessing parents’ motivation to have their children vaccinated. Vaccines. 2023;11(7):1192. pmid:37515008
  18. 18. Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: An introduction to theory and research. 1975. Reading, MA: Addison-Wesley.
  19. 19. Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991;50(2):179–211.
  20. 20. Gollwitzer PM. Implementation intentions: Strong effects of simple plans. American Psychologist. 1999;54(7):493.
  21. 21. Fishbein M, Ajzen I. Predicting and changing behavior: The reasoned action approach: Taylor & Francis; 2011.
  22. 22. Limbu YB, Gautam RK, Zhou W. Predicting vaccination intention against COVID-19 using theory of planned behavior: A systematic review and meta-analysis. Vaccines. 2022;10(12):2026. pmid:36560436
  23. 23. Chu H, Liu S. Integrating health behavior theories to predict American’s intention to receive a COVID-19 vaccine. Patient Education and Counseling. 2021;104(8):1878–86. pmid:33632632
  24. 24. Shmueli L. Predicting intention to receive COVID-19 vaccine among the general population using the health belief model and the theory of planned behavior model. BMC Public Health. 2021;21(1):1–13. pmid:33902501
  25. 25. Hossain MB, Alam MZ, Islam MS, Sultan S, Faysal MM, Rima S, et al. Health belief model, theory of planned behavior, or psychological antecedents: What predicts COVID-19 vaccine hesitancy better among the Bangladeshi adults? Frontiers in Public Health. 2021;9:711066. pmid:34490193
  26. 26. Reyes CT, Cao W, Astorini AG, Drohan MM, Schulz CT, Shuster CL, et al. Using the theory of planned behavior to assess willingness and attitudes towards COVID-19 vaccination among a predominantly white US college sample. Health Psychology and Behavioral Medicine. 2023;11(1):2248236. pmid:37601893
  27. 27. Fan C-W, Chen I-H, Ko N-Y, Yen C-F, Lin C-Y, Griffiths MD, et al. Extended theory of planned behavior in explaining the intention to COVID-19 vaccination uptake among mainland Chinese university students: An online survey study. Human Vaccines & Immunotherapeutics. 2021;17(10):3413–20. pmid:34170792
  28. 28. Li J-B, Lau EYH, Chan DKC. Why do Hong Kong parents have low intention to vaccinate their children against COVID-19? Testing health belief model and theory of planned behavior in a large-scale survey. Vaccine. 2022;40(19):2772–80. pmid:35339306
  29. 29. Dou K, Yang J, Wang L-X, Li J-B. Theory of planned behavior explains males’ and females’ intention to receive COVID-19 vaccines differently. Human Vaccines & Immunotherapeutics. 2022;18(5):2086393. pmid:35749588
  30. 30. Wang J, Li T, Ge J, Zhou M, Walker AN, Chen J, et al. Applying two behavioral theories to predict the willingness to receive COVID-19 vaccine booster in the elderly: A cross-sectional study. Research in Social and Administrative Pharmacy. 2023;19(3):495–501. pmid:36357271
  31. 31. Bui HN, Duong CD, Vu NX, Ha ST, Le TT, Vu TN. Utilizing the theory of planned behavior to predict COVID-19 vaccination intention: A structural equational modeling approach. Heliyon. 2023;9(6). pmid:37366521
  32. 32. Callow MA, Callow DD. Older adults’ behavior intentions once a COVID-19 vaccine becomes available. Journal of Applied Gerontology. 2021;40(9):943–52. pmid:34036821
  33. 33. Hagger MS, Hamilton K. Predicting COVID‐19 booster vaccine intentions. Applied Psychology: Health and Well‐Being. 2022;14(3):819–41. pmid:35193171
  34. 34. Berg MB, Lin L. Predictors of COVID-19 vaccine intentions in the United States: the role of psychosocial health constructs and demographic factors. Translational Behavioral Medicine. 2021;11(9):1782–8. pmid:34293163
  35. 35. Guidry JP, Laestadius LI, Vraga EK, Miller CA, Perrin PB, Burton CW, et al. Willingness to get the COVID-19 vaccine with and without emergency use authorization. American Journal of Infection Control. 2021;49(2):137–42. pmid:33227323
  36. 36. Hayashi Y, Romanowich P, Hantula DA. Predicting intention to take a COVID-19 vaccine in the United States: Application and extension of theory of planned behavior. American Journal of Health Promotion. 2022;36(4):710–3. pmid:35041541
  37. 37. Ekinci Y, Gursoy D, Can AS, Williams NL. Does travel desire influence COVID-19 vaccination intentions? Journal of Hospitality Marketing & Management. 2022;31(4):413–30.
  38. 38. Grootens-Wiegers P, Hein IM, van den Broek JM, de Vries MC. Medical decision-making in children and adolescents: Developmental and neuroscientific aspects. BMC Pediatrics. 2017;17(1):1–10. pmid:28482854
  39. 39. Maccoby EE. Parenting and its effects on children: On reading and misreading behavior genetics. Annual Review of Psychology. 2000;51(1):1–27. pmid:10751963
  40. 40. Willis DE, Andersen JA, Bryant‐Moore K, Selig JP, Long CR, Felix HC, et al. COVID‐19 vaccine hesitancy: Race/ethnicity, trust, and fear. Clinical and Translational Science. 2021;14(6):2200–7. pmid:34213073
  41. 41. Nápoles AM, Stewart AL, Strassle PD, Quintero S, Bonilla J, Alhomsi A, et al. Racial/ethnic disparities in intent to obtain a COVID-19 vaccine: A nationally representative United States survey. Preventive Medicine Reports. 2021;24:101653. pmid:34868830
  42. 42. Nguyen LH, Joshi AD, Drew DA, Merino J, Ma W, Lo C-H, et al. Racial and ethnic differences in COVID-19 vaccine hesitancy and uptake. medrxiv [Preprint]. 2021. pmid:33655271
  43. 43. Goldman RD, Ceballo R, Group ICPAS. Parental gender differences in attitudes and willingness to vaccinate against COVID‐19. Journal of Paediatrics and Child Health. 2022;58(6):1016–21. pmid:35170115
  44. 44. He K, Mack WJ, Neely M, Lewis L, Anand V. Parental perspectives on immunizations: impact of the COVID-19 pandemic on childhood vaccine hesitancy. Journal of Community Health. 2022:1–14. pmid:34297272
  45. 45. Morales DX, Beltran TF, Morales SA. Gender, socioeconomic status, and COVID‐19 vaccine hesitancy in the US: an intersectionality approach. Sociology of Health & Illness. 2022;44(6):953–71. pmid:35500003
  46. 46. Bonett S, Lin W, Sexton-Topper P, Wolfe J, Golinkoff J, Deshpande A, et al. Assessing and improving data integrity in web-based surveys: Comparison of fraud detection systems in a COVID-19 study. JMIR Formative Research. 2024;8:e47091. pmid:38214962
  47. 47. Teitcher JE, Bockting WO, Bauermeister JA, Hoefer CJ, Miner MH, Klitzman RL. Detecting, preventing, and responding to “fraudsters” in internet research: Ethics and tradeoffs. Journal of Law, Medicine & Ethics. 2015;43(1):116–33. pmid:25846043
  48. 48. Bauermeister JA, Pingel E, Zimmerman M, Couper M, Carballo-Dieguez A, Strecher VJ. Data quality in HIV/AIDS web-based surveys: Handling invalid and suspicious data. Field Methods. 2012;24(3):272–91. pmid:23180978
  49. 49. Centers for Disease Control and Prevention. CDC recommends pediatric COVID-19 vaccine for children 5 to 11 years. 2021. Available from: https://www.cdc.gov/media/releases/2021/s1102-PediatricCOVID-19Vaccine.html#:~:text=CDC%20Recommends%20Pediatric%20COVID%2D19%20Vaccine%20for%20Children%205%20to%2011%20Years,-Print&text=Today%2C%20CDC%20Director%20Rochelle%20P,the%20Pfizer%2DBioNTech%20pediatric%20vaccine.
  50. 50. Rosseel Y. lavaan: An R package for structural equation modeling. Journal of Statistical Software. 2012;48:1–36.
  51. 51. Beaujean AA. Latent variable modeling using R: A step-by-step guide: Routledge; 2014.
  52. 52. Cheung MW-L, Chan W. Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods. 2005;10(1):40. pmid:15810868
  53. 53. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science. 2015;43:115–35.
  54. 54. Lt Hu, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal. 1999;6(1):1–55.
  55. 55. Shi D, Lee T, Maydeu-Olivares A. Understanding the model size effect on SEM fit indices. Educational and Psychological Measurement. 2019;79(2):310–34. pmid:30911195
  56. 56. Schumacker RE, Lomax RG. A beginner’s guide to structural equation modeling: Psychology press; 2004.
  57. 57. Zintel S, Flock C, Arbogast AL, Forster A, von Wagner C, Sieverding M. Gender differences in the intention to get vaccinated against COVID-19: A systematic review and meta-analysis. Journal of Public Health. 2023;31(8):1303–27. pmid:35018277
  58. 58. Malik AA, McFadden SM, Elharake J, Omer SB. Determinants of COVID-19 vaccine acceptance in the US. EClinicalMedicine. 2020;26. pmid:32838242
  59. 59. Khubchandani J, Macias Y. COVID-19 vaccination hesitancy in Hispanics and African-Americans: A review and recommendations for practice. Brain, Behavior, & Immunity—Health. 2021;15:100277. pmid:34036287
  60. 60. Van Buuren S, Brand JP, Groothuis-Oudshoorn CG, Rubin DB. Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation. 2006;76(12):1049–64.
  61. 61. Rubin DB. Multiple imputation for nonresponse in surveys: John Wiley & Sons Inc., New York.; 1987.
  62. 62. Bogart LM, Ojikutu BO, Tyagi K, Klein DJ, Mutchler MG, Dong L, et al. COVID-19 related medical mistrust, health impacts, and potential vaccine hesitancy among Black Americans living with HIV. Journal of Acquired Immune Deficiency Syndromes. 2021;86(2):200. pmid:33196555
  63. 63. Brewer NT, Chapman GB, Rothman AJ, Leask J, Kempe A. Increasing vaccination: Putting psychological science into action. Psychological Science in the Public Interest. 2017;18(3):149–207. pmid:29611455
  64. 64. Peretti-Watel P, Larson HJ, Ward JK, Schulz WS, Verger P. Vaccine hesitancy: clarifying a theoretical framework for an ambiguous notion. PLoS Currents. 2015;7. pmid:25789201
  65. 65. Latkin CA, Dayton L, Yi G, Colon B, Kong X. Mask usage, social distancing, racial, and gender correlates of COVID-19 vaccine intentions among adults in the US. PLOS One. 2021;16(2):e0246970. pmid:33592035