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Abstract

Coronavirus disease 2019 (COVID-19) vaccine hesitancy is a major concern in this pandemic context. This study postulates that vaccine hesitancy among individuals might be associated with a high state of decisional conflict which indicates a state of delayed decision-making. This study aimed to identify the factors related to COVID-19 vaccine hesitancy and examine the relationship between COVID-19 vaccine hesitancy and decisional conflict by focusing on 3 sub-factors: value, informed, and support. This cross-sectional study administered an online, self-administered survey to people aged over 20 years old who were living in Japan using an online self-administered survey. To clarify the association between hesitancy and decisional conflict for the first or second vaccination, this study compared the hesitant and non-hesitant groups. Multivariate analysis was conducted to determine which sub-factor contributing to decisional conflict was associated with vaccine hesitancy. A total of 527 responses were included in the analyses. For the first vaccination (n = 527), women and individuals in their 30s were more hesitant. For the second vaccination (n = 485), women, and individuals in their 40s, non-medical individuals, and individuals without any past history were more hesitant. No significant differences were found for employment status, household composition, convulsions history, allergies, or influenza vaccine hesitancy. For vaccine hesitancy and decisional conflict, a moderate positive correlation was found and means were significantly higher for the hesitant group. Unclear values and limited supported were positively associated with vaccine hesitancy. Eliminating decision-making conflicts can effectively reduce vaccine hesitancy. Furthermore, the findings suggest that it is insufficient to merely provide information. Thus, clarifying the value and providing tangible support from the administration is desirable.

Keywords

vaccination hesitancy decision-making decisional conflict COVID-19 vaccines

What do we already know about this topic?

Vaccine hesitancy is a global health concern characterized by a delay in decision-making regarding vaccination. People with vaccine hesitancy are likely to experience higher levels of decisional conflict. it is important to reduce this hesitancy to curb the spread of infectious diseases. Vaccination hesitancy varies by type, region, and culture.

How does your research contribute to the field?

Vaccine hesitancy is influenced by several factors. In this study, we identified that resolving decisional conflicts can be an effective strategy for reducing vaccine hesitancy. Furthermore, it aims to intervene and clarify the value of vaccination, and provide tangible support, because value and support are subscales of decisional conflict.

What are your research’s implications toward theory, practice, or policy?

The findings can help in the consideration of policies for adults, especially women and people with high levels of hesitancy. To promote decision-making among those who are hesitant about COVID-19 vaccination, it is important to emphasize the value of vaccination and offer concrete support.

Introduction

Vaccination against coronavirus disease 2019 (COVID-19) is considered one of the most effective ways to prevent morbidity and mortality during the ongoing pandemic.^1,2 Since the COVID-19 vaccinations were first administered, several countries have made various efforts to increase vaccination rates, including providing the vaccine free of charge.^3-6 However, prior studies have reported a 6% to 37% resistance and 25% to 36% hesitation toward COVID-19 inoculation.^7-10 Consequently, this presents a serious concern in the effort to contain the pandemic.¹¹

The World Health Organization (WHO) has identified vaccine hesitancy as one of the “10 threats to global health in 2019.”⁹ The Strategic Advisory Group of Experts on Vaccine Hesitancy working group of the WHO defined vaccine hesitancy as a “delay in acceptance or refusal of vaccines despite availability of vaccine services.” The advisory group explained that vaccine hesitancy is complex and situation-specific, varies by region and vaccine type, and results from delays in the vaccine decision-making process.¹⁰

Individual decision-making regarding vaccination is a complex process that depends on several factors including cognitive, emotional, cultural, social, spiritual, and political factors.¹² Therefore, interventions on cognitive factors may improve vaccine hesitancy.¹² While changing the intentions of individuals with strong beliefs (eg, refusal or resistance) toward vaccination is challenging, hesitation may be addressed through public health messaging.⁶ Decreasing vaccine hesitancy requires a focus on understanding the public’s perceptions of decision-making.¹²

Decisional conflict is a perception of decision-making that describes a state of uncertainty about an individual’s course of action. When decisional conflict is high, one of the behavioral aspects is delayed decision-making.^13,14 Decisional conflict refers to a situation in which there remains uncertainty surrounding the required course of action. Furthermore, it is more likely to occur when: a person is faced with a decision that involves risk or an uncertain outcome, a person has to make a choice involving the immense potential for gains and losses, or there are concerns about later regret stemming from the chosen option becoming less beneficial than the others.¹³ The decisional conflict scale (DCS) developed by O’Connor comprises 3 concepts: “uncertainty,” which is considered the main concept of decisional conflict; “factors contributing to the uncertainty”; and “effective decision-making,” which is the resulting behavior. Moreover, “factors contributing to uncertainty” has 3 subscales: information, value, and support.¹⁵ “Informed” refers to feeling uninformed about choices, benefits, and risks. Information about choices, benefits, risks, and side effects allows people to feel more informed. “Value” indicates the lack of clarity about personal values. People are more likely to judge the value if the outcomes (ie, physical, emotional, and social effects) are explained in sufficient detail. “Support” describes the lack of support in making a choice and feeling pressured to choose an action. People may feel more supported if they are guided or coached during the steps of deliberation and shared decision-making.^13,15 Addressing issues related to these sub-concepts can help reduce uncertainty in decision-making, thus increasing the likelihood of smooth and effective decisions.

As a conceptual framework for this study, we focused on the association between decisional conflict and vaccine hesitancy. Vaccine hesitancy is influenced by delayed decision-making regarding the process of vaccine acceptance or rejection.¹⁰ Presumably, decisional conflict over decision-making regarding vaccination and delays influence vaccine hesitancy. In previous studies, DCS was measured and demonstrated an association with the acceptance of influenza and human papillomavirus vaccines.^16-18 The purpose of this study was to investigate the factors related vaccine hesitancy and the relationship between vaccine hesitancy and decisional conflict, and to obtain suggestions to reduce COVID-19 vaccine hesitancy.

Methods

Study Design, Setting, and Participants

This cross-sectional study recruited Japanese people aged over 20 years old. The participants were surveyed using a self-administered web-based questionnaire survey with snowball sampling. The age and number of medical staff were adjusted when the initial sample of acquaintances was selected to reduce bias in the sample demographics to the furthest extent.

Sampling Procedures

The initial samples comprised 50 acquaintances of the author, 10 from each generation in 10-year intervals (ie, 20-29, 30-39, etc.). Each generation was further adjusted to ensure that medical staff comprised approximately half of these 10 individuals. The initial samples were contacted through e-mail or social networking services, such as the “LINE app,” which is a chat tool widely used in Japan. The URL for the questionnaire was provided to the first target group of 60 participants and acquaintances of the researchers. Thereafter, the first target group was asked to send the URL of the questionnaire to their acquaintances. This process was further repeated until responses reached 550 participants or the deadline.

The questionnaire commenced with an explanation of the study’s purpose, methods, declaration for voluntary participation, and privacy protection. By clicking on the “I agree” button on the web survey page to continue, participants confirmed that they agreed to participate in the study. The study was conducted from August 12 to September 30, 2021 (Figure 1).

Figure 1.

The target population’s first vaccinations and number of first and second vaccine hesitancies.

Data Collection

The questionnaire included items on participant demographics, the first and second instances of COVID-19 vaccine hesitancy, and decisional conflicts about COVID-19 vaccination. Participant demographics included age, gender, employment status, medical staff or not, household composition, past history, history of convulsions, history of severe allergies, influenza vaccine hesitancy, and COVID-19 vaccination status. Furthermore, the participants’ ages were requested and categorized in decades (eg, 20s, 30s, etc.). Participants were asked about COVID-19 vaccine hesitancy with the question “Are/Were you hesitant to the first and/or second vaccination against COVID-19?” The participants had to respond with either “yes” or “no.”

Decisional conflict was measured using the DCS developed by O’Connor.¹³ This scale has been translated into many languages and used in various contexts.^19,20 We used the Japanese version of the DCS, for which the reliability and validity were confirmed.²¹ The Japanese version of the DCS comprises 16 items. The answers are measured using a five-point Likert scale consisting of 5 constructs: “uncertainty,” “informed,” “support,” “values,” and “effective decision.” Responses to each item are scored from 1 (strongly agree) to 5 (strongly disagree) with total scores ranging from 16 to 90 points. The total DCS score is calculated and standardized by summing all items, dividing them by 16, and then multiplying that by 25. Similarly, since each subscale (ie, informed, support, and values) consists of 3 items, they are divided by 3 and then multiplied by 25. Higher scores indicate less information, value, support, and less effective decision making. Consequently, they indicate higher levels of decisional conflict. Cronbach’s alpha is high in all domains (ranging from .84 to .96).²²

Sample Size and Power

We calculated our sample size using G-Power 3.1 and found that 518 participants were needed for effect size (d) of 0.3, an alpha level of .05 (2 tails), power of 0.8, and an allocation ratio of 0.3 with a Mann-Whitney U test.^23,24 We aimed to collect data on 550 participants, considering missing values.

Analysis

The first instance of vaccine hesitancy was analyzed for all samples that fully responded to the questionnaire. Analysis of the second vaccine hesitancy included individuals who had completed or planned to complete the first vaccination. Participants were divided into COVID-19 vaccine hesitancy and non-hesitancy groups for each analysis. Age, gender, medical staff or not, past history, employment status, family type, history of convulsions, allergies, vaccination status, and influenza vaccine hesitation were compared using Chi-squared and Fisher’s exact tests. For items that had a significant difference, the effect size was calculated by post-hoc analysis using G-Power 3.1. Total DCS scores were evaluated for normality using a Shapiro-Wilk test after standardization and a non-parametric test was adopted. The reliability of the scale was evaluated with Cronbach’s alpha.

To find the association between COVID-19 vaccine hesitancy and decisional conflict, the correlation coefficient between vaccine hesitancy and standardized DCS total score was calculated. The DCS total scores and each sub-factors of hesitancy and non-hesitancy groups were compared using the Mann-Whitney U test.

A binomial logistic regression analysis was conducted to determine which of the total score of each DCS sub-scale was associated with vaccine hesitancy at the first or second instance of vaccination using vaccine hesitancy as the dependent variable. The 3 subscales, namely, informed, values, and support are considered independent variables which may change owing to the intervention. Uncertainty was excluded because it describes a state instead of a factor and it is highly correlated with all the other total score of sub-scales. “Effective decision” was also excluded because it indicates the evaluation of the quality of choice. Adjustment variables included age, gender, medical staff or not, past history, history of convulsions, severe allergies, and influenza vaccine hesitancy, which have been associated with vaccine hesitancy in previous studies.^25-28 Multicollinearity was confirmed by correlation coefficients (r > .8).

Analyses were performed using SPSS version 28 (IBM, NY, 2021).

Ethical Considerations

The participants were informed of the purpose and methods of the study at the beginning of the web-based questionnaire. Furthermore, they were informed that: their free will would be respected while participating in the study, their personal information would be protected, and their responses would not be used for any purpose other than the study. The researcher obtained their informed consent before the study. This study was conducted after obtaining approval from the Institutional Review Board of Tokyo Medical and Dental University School of Medicine (approval number M2021-091)

Results

Participants’ Characteristics and Factors Related to COVID-19 Vaccine Hesitancy

A total of 529 people responded to the questionnaire. Among these participants, 527 consented to the study and were included in the vaccine hesitancy analysis at the first instance of vaccination. Subsequently, the 485 participants who completed the first analysis were included in the second instance of vaccine hesitancy analysis. Table 1 provides a comparison of the hesitant and non-hesitant groups of participants’ characteristics for each instance of vaccine hesitancy.

Table 1.

Participants’ Characteristics and Differences Between COVID-19 Vaccination Hesitancy and Non-hesitancy.

	First vaccination (n = 527)				Second vaccination (n = 485)
	Hesitant	Non-hesitant			Hesitant	Non-hesitant
	169	358			99	386
	(32.1%)	(67.9%)	χ2	p	(20.4%)	(79.6%)	χ2	p
Age			11.87	.04^‡			10.66	.03^‡
20-29	55	129			34	139
30-39	21_a	30_a			11	33
40-49	20	33			15_a	30_a
50-59	65	121			35	135
Over 60	8_b	45_b			4_b	49_b
Gender			24.82	<.001			12.51	<.001
Men	29	139			17	138
Women	140	219			82	248
Employment status				.18^†				n.s.^†
Regular employment	77	177			23	111
Part-time employment	34	44			46	181
Full-time stay-at-home spouse	15	28			8	31
Student	39	100			21	51
Unemployed	4	9			1	12
Medical staff or not			1.52	.22			3.71	.05
Medical staff	61	151			33	170
Not medical staff	108	207			66	216
Household composition			.07	.97				n.s.
Living with relatives under 65	101	217			23	99
Living with relatives over 65	25	50			63	231
Living alone	43	91			13	56
Past history			1.36	.24			5.78	.02
Any past history	18	53			6	59
No past history	151	305			93	327
Convulsions history			.44	.51				n.s.^†
Any convulsion history	6	19			2	23
No convulsion history	163	339			97	363
Allergy			.94	.33				n.s.
Any allergy	10	13			94	370
No allergy	159	345			5	16
Influenza vaccinate hesitancy			3.10	.08				n.s.
Hesitant	25	33			13	37
Non-hesitant	144	325			86	349
Vaccination status
First COVID-19 vaccine			35.52	<.001^‡
Vaccinated (including intention)	139_c	346_c
Not vaccinated	16_d	10_d
Undecided	14_d	2_d
Second COVID-19 vaccination								<.001^‡
Vaccinated (including intention)					96_c	385_c
Not vaccinated				0_d	1_d
Undecided				3_d	0_d

Note. Chi-square test was used consistently unless otherwise noted.

COVID-19 = coronavirus disease 2019; n.s. = no significance.

†

Fischer’s exact test used.

‡

Significance level was P < .05 according to the adjusted P-value using the Bonferroni correction.

Group differs statistically significantly from (in column) where b is indicated.

Group differs statistically significantly from (in column) where d is indicated.

Statistically significance difference: P < .05.

The first instance of vaccine hesitancy comprised the hesitant (n = 169) and the non-hesitant groups (n = 358), where a significant sex difference was found, with women exhibiting higher vaccine hesitancy, χ² (1, n = 527) = 24.82, P < .001. Furthermore, a significant difference was found between participants aged in their 30s and over 60 years, χ² (4, n = 527) = 11.87, P = .04, with more hesitancy among people in their 30s. However, no significant differences in employment status, medical staff or not, household composition, past history, convulsions history, allergy, and influenza vaccine hesitancy.

The second instance of vaccine hesitancy comprising the hesitant group (n = 99) and the non-hesitant group (n = 386) also found that women, χ² (1, n = 485) = 12.51, P < .001, and participants in their 40s exhibited significantly higher vaccine hesitancy than participants aged over 60 years, χ²(4, n = 485) = 10.66, P = .03. Furthermore, a significant difference in their past history was found, χ² (1, n = 485) = 5.78, P = .03, as these individuals exhibited less hesitancy. Moreover, a marginally significant difference in medical staff or not was observed, χ² (1) = 3.75, P = .07. However, no significant differences were found in employment status, household composition, convulsions history, allergy, and influenza vaccination hesitancy.

Regarding vaccination status, both instances of vaccine hesitancy yielded a significant difference, P < .001, in “vaccinated/intentionally vaccinated” versus “not vaccinated” and “undecided,” with the non-hesitant group demonstrating more vaccinations.

Association Between Vaccine Hesitancy and Decisional Conflict

Cronbach’s alpha for the total score of the DCS was .93 with sub-scales alpha also ranging from .75 to .87. The correlation coefficients between the first vaccine hesitancy and DCS total score were .57. Table 2 shows the difference in DCS scores between the vaccine-hesitant and non-hesitant groups. A Mann-Whitney U test was conducted to test the hypothesis that the hesitant group would have higher mean scores on the decisional conflict. For the first instance of vaccine hesitancy, the results of the tests indicated that hesitant group had an average rank of 378.33 in the DCS total score, while non-hesitant group had an average rank of 210.03, in the expected direction and significant, z = 11.85, P < .001. The results of the second instance of vaccine hesitancy were also in the expected direction and significant, z = 7.48, P < .001. Hesitant group had an average rank of 336.91, whereas non-hesitant group had an average rank of 218.91. Similarly, significant differences were reported in all total score of sub-scales with the hesitant group scoring higher for both hesitancy of the first and second vaccination.

Table 2.

Differences Between COVID-19 Vaccine Hesitancy and No Vaccine Hesitancy in Decisional Conflict Using the Mann-Whitney U Test.

	First vaccination (n = 527)
	Hesitant (n = 169)			Non-hesitant (n = 358)
	Mean (SD)	Mdn	Mean rank	Mean (SD)	Mdn	Mean rank	Z	p	ES (d)
DCS total	40.32 (15.31)	40.63	378.33	21.65 (14.17)	22.66	210.03	11.85	<.001	1.27
Sub-scales
Uncertainty	50.05 (22.08)	50.00	386.47	20.96 (18.17)	25.00	206.19	12.82	<.001	1.43
Informed^a	28.55 (16.31)	25.00	313.89	20.83 (14.44)	25.00	240.45	5.40	<.001	.50
Value^a	35.21 (20.37)	33.33	340.58	20.44 (17.12)	25.00	227.85	8.14	<.001	.79
Support^a	48.27 (23.06)	50.00	350.65	28.51 (20.17)	25.00	223.09	9.04	<.001	.91
Effective-decision	39.72 (19.03)	37.50	372.68	18.61 (15.37)	18.75	212.70	11.35	<.001	1.22
	Second vaccination (n = 485)
	Hesitant (n = 99)			Non-hesitant (n = 386)
	Mean (SD)	Mdn	Mean Rank	Mean (SD)	Mdn	Mean Rank	Z	p	ES (d)
DCS total	38.26 (16.91)	37.50	336.91	23.70 (15.04)	25.00	218.91	7.48	<.001	.91
Sub-scales
Uncertainty	46.89 (24.70)	50.00	341.92	24.46 (20.33)	25.00	217.63	7.97	<.001	.99
Informed^a	25.76 (16.50)	25.00	268.26	21.78 (14.90)	25.00	236.52	2.11	.035	.25
Value^a	32.74 (21.17)	25.00	301.17	22.02 (17.19)	25.00	228.08	4.76	<.001	.56
Support^a	48.57 (23.42)	50.00	328.88	30.27 (21.04)	25.00	220.97	6.89	<.001	.82
Effective-decision	37.56 (18.61)	37.50	338.1	20.89 (16.74)	25.00	218.61	7.64	<.001	.94

Note. All the DCS scores were standardized.

DCS = decision conflict scale; ES = effect size.

Factors contributing uncertainty.

Factors Related to COVID-19 Vaccine Hesitancy

Table 3 shows the odds ratios, for which sub-factors are more related to vaccine hesitancy: informed, value, or support as confirmed by logistic regression analyses. Consequently, no multi-collinearity was found.

Table 3.

Factors Related to COVID-19 Vaccine Hesitancy Among the Factors Contributing to Uncertainty Using Multivariate Analyses.

		First instance of vaccine hesitancy (n = 527)					Second instance of vaccination hesitancy (n = 485)
		β	OR	95% CI		p	β	OR	95% CI		p
		β	OR	LL	UL	p	β	OR	LL	UL	p
Age	20-39 Reference
	30-39	.47	1.60	.75	3.43	n.s.	.27	1.30	.54	3.15	n.s.
	40-49	.43	1.54	.72	3.31	n.s.	.57	1.77	.75	4.18	n.s.
	50-59	.33	1.39	.80	2.41	n.s.	.00	1.00	.53	1.88	n.s.
	Over 60	–.73	.48	.19	1.25	n.s.	–.97	.38	.11	1.25	n.s.
Gender		1.36	3.90	2.27	6.70	<.001	1.16	3.18	1.67	6.05	<.001
Medical staff or not		–.12	.89	.54	1.44	n.s.	–.61	.55	.31	0.97	.04
Past history		–.05	.95	.48	1.90	n.s.	–.73	.48	.18	1.26	n.s.
Convulsions history		–.26	.77	.26	2.26	n.s.	–.90	.41	.09	1.88	n.s.
Allergy		.35	1.42	.51	3.95	n.s.	–.19	.82	.25	2.74	n.s.
Influenza vaccine hesitancy		.43	1.53	.81	2.92	n.s.	.26	1.30	.61	2.78	n.s.
Factors of DCS
	Informed	–.01	.99	.97	1.01	.18	–.03	.97	.95	.99	.02
	Value	.04	1.04	1.02	1.05	<.001	.03	1.03	1.01	1.05	.01
	Support	.03	1.03	1.02	1.05	<.001	.03	1.03	1.02	1.05	<.001

Note. A logistic regression model with vaccine hesitancy as the dependent variable and adjusted by the participants’ basic information.

OR = odds ratio; CI = confidence interval; LL = lower limit; UL = upper limit; n.s. = no significance.

The first instance of vaccine hesitancy indicated that value (β = .04, OR = 1.04, P < .001) and support (β = .03, OR = 1.03, P < .001) were positively associated with vaccine hesitancy. However, no significant differences were found for the informed. Among the adjustment variables, a significant gender difference was found, with higher vaccine hesitancy among women (β = 1.35, OR = 3.87, P < .001).

The second instance of vaccine hesitancy found that value (β = .03, OR = 1.03, P = .01) and support (β = .03, OR = 1.03, P < .001) were also positively associated. However, the total score of informed sub-factor (β = -.03, OR = 0.97, P = .02) was negatively associated with vaccine hesitancy. Among the adjustment variables, women also exhibited significantly higher vaccine hesitancy (β = 1.16, OR = 3.19, P < .001). Furthermore, a significant difference in medical staff or not was observed, with medical staff demonstrating less hesitancy toward COVID-19 vaccination.

Discussion

Consistent with previous studies, the current results indicated that women showed more hesitancy to vaccinate than men.²⁹ These findings are possibly influenced by slightly higher adverse events after immunization among women.^25,30 In addition, pregnant women were not included in the clinical trials of the vaccine before its approval. Consequently, discussions regarding the safety of the vaccine for pregnant women are insufficient.^31-33 Furthermore, studies indicate that pregnant women have some anxiety and hesitancy toward vaccination.^34-37 The first instance of vaccine hesitancy found that significantly more people in their 30s and 40s were hesitant to receive the vaccine in contrast to individuals in their 60s. The hesitancy could be partly attributed to side effects that are said to be stronger in younger age groups.^25,38 Furthermore, many individuals in their 30s and 40s are in their child-rearing generation. Moreover, these individuals became ill because of the side effects of the vaccine, and they cannot take time off or find someone to take care of their children. Consequently, this may have influenced their vaccine hesitancy.

This study showed a positive correlation between COVID-19 vaccine hesitancy and decisional conflict. While the correlation coefficient of .57 is not very high, it is considered a moderate positive correlation.³⁹ The comparisons of the mean DCS scores were higher in the hesitant group in contrast to the non-hesitant group, suggesting that participants with vaccine hesitancy were in a state of high decisional conflict. Furthermore, the informed, value, and support constructs were also positively associated with COVID-19 vaccine hesitancy. These 3 constructs are determinants of uncertainty which is a key concept against decision-making conflict, and factors with the potential to intervene. Thus, working on the factors of decisional conflict may be an effective strategy to reduce vaccine hesitancy.^16,17

Multivariate analyses showed that interventions to value and support were more effective (see Table 3). Thus, it may be effective to present clear advantages and disadvantages to vaccination targets to apprise the participants of the value of vaccination through advice and support from others. However, it was found that the informed total score of sub-scale affected vaccine hesitancy slightly negatively. This is likely because of the misinformation available on the Internet and social media, which inhibits decision-making, and the influence of other factors as indicated by the multivariate analysis. Thus, this needs to be carefully considered. Healthcare professionals need to establish communication with vaccine targets using correct knowledge.^40,41 Furthermore, healthcare professionals could use decision support tools designed for health-related or social contexts (eg, Ottawa Personal Decision Guide⁴²) to facilitate shared decision-making.⁴³

To alleviate vaccine hesitancy and encourage vaccination among young people and women, mere publicity and information provision are not sufficient. In addition to sharing information, providing administrative support such as consultation services may be an effective method to provide free vaccines and improve decision-making, such as operating consultation services. Furthermore, administrative support can compensate for the potential disadvantages, such as public leave after vaccination, reduced working hours, free babysitting services, and free temporal nurseries. Moreover, the government that provides vaccines may be required to provide adequate compensation for rare vaccine injuries and to implement a support system to manage the involved stakeholders.^44-46

Limitations and Scope for Future Studies

This study used a web-based survey following snowball sampling because of its feasibility, and it was not known how many people accessed the URL for the survey. Thus, the population could not be identified which limits the generalizability of the data. Furthermore, the survey did not include adverse events following immunization from the first vaccination or previous history of COVID-19 in the questionnaire. Therefore, these might have influenced second vaccination hesitancy.

This survey revealed the relationship between vaccine hesitancy and decisional conflict, but it is impossible in a cause-and-effect relationship. In addition, 139 of the 169 hesitant group members in this study had received the vaccine. The participants might have received the vaccination for some value or support, but the specific reasons remain unclear.

Further research is required to determine the causes of hesitation and interventions that may be effective in reducing vaccine hesitancy when considering specific decision-support initiatives. Furthermore, vaccine hesitancy is specific to each vaccine. Thus, it is not possible to mention vaccines other than the COVID-19 vaccine. However, the association between vaccine hesitancy and decisional conflict may help in the future when a vaccine response is needed for another emerging infectious disease.

Conclusion

This study suggests that intervention in decisional conflicts may reduce COVID-19 vaccine hesitancy. Furthermore, this study found that women and participants in their 30s and 40s were more likely to be hesitant despite their status of engagement in health care.

Improving vaccine hesitancy may not be possible by merely providing information. This study indicates that reinforcing the value and support for vaccination through the presentation of tangible benefits and public sponsorship could reduce vaccine hesitancy.

Footnotes

Acknowledgements

We thank all participants who participated in this study despite the ongoing pandemic. We would also like to thank Ayumi Toda for her support throughout this study.

Author Contributions

NH: Study concept and design, analysis and interpretation of data, and writing. NO: Study concept and design and interpretation of data. NT: Study concept and design, acquisition of participants, and data collection. JM: Study concept and design and interpretation of data. All authors reviewed and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The author(s) received the operating grant from Department of Reproductive Health Nursing, Graduate School of Health Care Sciences, Tokyo Medical and Dental University (TMDU).

ORCID iDs

Natsuko Hiroyama

Noriko Okubo

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Reducing COVID-19 Vaccine Hesitancy From a Decisional Conflict Model: A Cross-Sectional Study

Abstract

Keywords

Introduction

Methods

Study Design, Setting, and Participants

Sampling Procedures

Data Collection

Sample Size and Power

Analysis

Ethical Considerations

Results

Participants’ Characteristics and Factors Related to COVID-19 Vaccine Hesitancy

Association Between Vaccine Hesitancy and Decisional Conflict

Factors Related to COVID-19 Vaccine Hesitancy

Discussion

Limitations and Scope for Future Studies

Conclusion

Footnotes

Acknowledgements

Author Contributions

Declaration of Conflicting Interests

Funding

ORCID iDs

References