COVID-19 Vaccine Acceptance and Hesitancy Among Healthcare Workers in Nigeria. A Mixed-Method Analysis Using the WHO 3C and BeSD Model

COVID-19 Vaccine Acceptance and Hesitancy Among Healthcare Workers in Nigeria. 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A Mixed-Method Analysis Using the WHO 3C and BeSD Model Pius Angioha, Chizoba Wonodi, Ikechukwu Okpe, Abdulrasheed Abdulraheem, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7696059/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Nigeria had enough COVID-19 vaccines to meet its set target, yet vaccine uptake challenges persisted, even among healthcare workers. Part of a broader study conducted in Ethiopia, Kenya, and Nigeria that explored COVID-19 vaccine behavior. This study adopted the 3C and BeSD model to examine COVID-19 vaccine acceptance and hesitancy among healthcare workers in Nigeria. We conducted a mixed-method study involving 654 healthcare workers across Nigeria from February 8 to March 30, 2022. Quantitative data was analyzed using R (version 4.1.1) in R-Studio, The statistical results produced were descriptive, bivariate, and multivariate analyses. The qualitative analysis involved using Dedoose software. 86% of the surveyed healthcare workers are vaccinated. Community health workers and medical doctors boast over 90% vaccination rates compared to other specialties. Multivariate analysis highlights convenience as the strongest driver of COVID-19 vaccination (adjOR = 7.72, 95% CI: 3.50–17.04, p < 0.01). Barriers to vaccination using the BeSD model include vaccine safety and efficacy concerns, relating to the thinking and feeling domain, followed by access to vaccines and a busy work schedule, which express practical barriers to vaccination. There is need for policy action to combat infodemics and ensure COVID-19 vaccine access for Nigerian healthcare workers. Figures Figure 1 1. Introduction Nigeria has made substantial gains in ensuring that its population has unrestricted access to the COVID-19 vaccines. But convincing the eligible adult population to accept the COVID-19 vaccine and achieving its set target of vaccinating 70% of the population by the end of 2022 has become a complex and multifaceted challenge for the government and its partners [1, 2]. As of November 2022, Nigeria had received over one hundred million doses of viable vaccines, with more than 50% already administered [3, 4]. COVID-19 vaccination in the country is still lagging, particularly among health workers. As frontline responders, health workers have been the unsung heroes in the fight against the spread of the COVID-19 virus. As first responders, they are exposed to the virus due to their direct contact with and proximity to infected individuals, making them a potential means for transmission to the wider community [5, 6]. Healthcare workers’ potential risk to the wider community underscores the need for all health workers to be vaccinated. Available data revealed that the Nigerian government and its agency, the NPHCDA, planned to vaccinate 2,114,933 healthcare workers by the end of 2021 [7]. But according to the [8], as of November 2022, Nigeria had only vaccinated 25% of its health workforce. The study by [9], that collected data from 422 healthcare workers in Abia State revealed that only 49.5% had received the vaccine. [10] study in the Northern, Western, and Eastern regions that collected data from 710 healthcare workers revealed that only 59.3% had received the COVID-19 vaccine. These data show that there is a gap between the actual target and the number of health workers who have been vaccinated and also that there is variability in the coverage estimates among health workers who have received the COVID–19 vaccine, which can be attributed to the population studied or the sampling procedure adopted. The data also shows that hesitancy among health workers is a problem in Nigeria. Evidence from previous studies also shows that Nigeria still lags behind other African countries in terms of vaccinating health workers. [11] found that 82.5 percent of health workers in Malawi have received at least the first dose of the COVID-19 vaccine. Another study in Ghana found that 78.6 percent of health workers have received the COVID-19 vaccine [12]. In Ethiopia, [13], in a systematic literature review, found that 54.6% of healthcare workers have been vaccinated. The Nigerian government and partners have implemented various measures to ensure that frontline workers are vaccinated. These measures include a mandate for all civil servants, including health workers, to be vaccinated, training programs, and recurring sensitization programs on the importance of vaccination for healthcare providers. Despite these efforts, the problem of hesitancy among healthcare workers persists. Reported vaccine hesitancy was around vaccine safety and negative information paddled around by the media [14 ], mistrust of government and vaccine manufacturer intentions, vaccine infodemics, and fear of harmful psychological consequences [15], issues around vaccine side effects, and the fear of the unknown [16 ]. Literature has provided evidence of studies assessing drivers of acceptance and barriers to COVID-19 vaccination among healthcare workers in Nigeria. None have applied the 3C or behavioral and social drivers (BeSD) model in Nigeria. The 3C and BeSD models are two frameworks developed by the World Health Organization (WHO) that have been applied to understanding people’s vaccination behavior as well as to developing tools to measure social and behavioral drivers of vaccination uptake [17-19] . The 3C model highlights 3 key factors that drive vaccination behaviour; (i) complacency, which refers to the perceived need for the vaccine; (ii) convenience, which refers to the ease of accessing the vaccines; and (iii) confidence, which refers to the trust in the vaccine's safety and efficacy [17] . The BeSD model identifies four key domains that individual, interpersonal, and social factors can be grouped into to understand the complex interplay of factors that influence vaccination behaviour; (i) thinking and feeling about vaccination; (ii) social processes that drive or inhibit vaccine behavior; (iii) motivation that drives or inhibits vaccine seeking behavior; and (iv) practical issues involved in seeking and receiving vaccination [18, 20] . Development partners and scholars have recently acknowledged the importance of the 3C and BeSD models as being readily understandable and able to be used to answer research questions that guide the development of intervention and risk communication strategies to improve vaccine uptake and address vaccine hesitancy [21, 22]. To address the knowledge gap in the literature regarding the application of 3C and BeSD models in analyzing COVID-19 vaccination in Nigeria, as well as using an updated survey representative of the healthcare worker population, this study aims to answer the following questions: Do acceptance and hesitancy of COVID-19 vaccines vary among healthcare workers in different specialties in Nigeria? What factors drive COVID-19 vaccination among healthcare workers in Nigeria? What barriers do healthcare workers perceive in relation to vaccination in Nigeria? 2. Materials and Methods 2.1. Study design This is part of a larger cross-country study in three Sub-Saharan countries of Ethiopia, Kenya, and Nigeria, that adopted a mixed method approach to collect data from policymakers, healthcare workers, and clients to understand their journey to COVID-19 vaccination. For this part of the study, we adopted a mixed method approach that employed the depth of qualitative data collected using In-depth Inter-views (IDIs) and Focus Group Discussions (FGD) to triangulate data from quantitative survey interviews. 2.2. Study setting The study was conducted in six randomly selected states from the six geopolitical zones that make up Nigeria. The six selected states represented states with low, medium, and high burdens of the COVID-19 infection rate in the country. The selected states are Enugu in the South-East, Rivers in the South-South, Lagos in the South-West, Kogi in the North-Central, Gombe in the North-East, and Kano in the North-West. 2.3. Sampling Procedure The target population for this part of the study are public and private health workers drawn from the three levels of the health care system in Nigeria. The three tiers are primary, secondary, and tertiary health systems. To arrive at the quantitative survey's sample size, Kelley and Maxwell's power analysis [ 23 ], was used to arrive at a precise population estimate of 316 at a 95% confidence level. The sample size was then inflated further by 67% to 527, rounded up to 600 to have a more representative sample of the population of health workers. Structured convenience sampling were used to select the health facilities. We acquired a list of all health facilities in the selected states from the Ministry of Health registry. These formed our sampling frame. We then matched the national ratio of primary, secondary, and tertiary health facilities in each state. This gave us a ratio of twelve (12) primary healthcare facilities, six (6) secondary and two (2) tertiary health facilities per state. Within each selected health facility, respondents were sampled using snowball sampling technique. This we did by first deploying trained data collectors to distribute the instrument using online means at each selected health facility. The link to the instrument was given to the head of each selected health facility, who distributed it to their colleague, with specific instruction to distribute to others in the facility. For each healthcare worker that was sent the online survey instrument, instruction was also given to distribute the survey instrument to eligible colleagues. This process was repeated until the sample size was reached. For the qualitative samples, convenient sampling was used to select 42 healthcare workers from the three (3) tiers of the health system across 6 states that represented the six geopolitical regions of Nigeria. We conducted 18 KII with health workers and 3 FGDs at the selected health system. Each FGD had 8 participants. 2.4. Instruments We used survey questionnaire and key informant interview and focus group discussion guide to collect data from the selected states. The tools were designed to elicit information on vaccination intention and motivation, vaccination training and advocacy, vaccination sentiment, convenience, confidence and complacency. 2.5. Data collection procedure We deployed the developed quantitative data collection tool into the online soft-ware Qualtrics, an integrated survey software that allows for easy survey creation, collection and analysis. The use of Qualtrics was out of the belief that a large percentage of healthcare workers are literate and have access to mobile internet. The survey link was first pre-tested in health facilities that were not part of the originally selected facility to check the validity of the survey questions. We then employed trained and experienced data collectors from the six states to distribute the online survey tool to the selected facilities. Between February 8 and March 30, 2022, data collectors distributed the link to each health facility. A minimum of five healthcare workers participated in the online survey from each health facility. The technical team provides real-time data monitoring as they come in and notify the health facility or data collectors once the state reaches the sample size. Qualitative interview guides designed by the research team to address the re-search question was first pre-tested by trained data collectors on healthcare workers in health facilities that are not part of the originally selected health facilities. Comments and feedback from the pre-test was then used to adjust the tool before commencing data collection. Across the six states, between February 10 and 18, 2022, 42 healthcare workers were interviewed. The interviews were conducted in both English and local Hausa, Yoruba and Igala dialects, lasting a minimum of 35 minutes. During each interview, with the consent of the respondents, we recorded all discussion and also took down notes to accurately capture information that is exchanged during the discussion. 2.6 Data analysis Responses from the online survey were cleaned using an Excel spreadsheet. All respondents who gave no consent, reported not being a health worker, or had incomplete responses were excluded from the analysis. We categorized responses according to the 3C model questions. Exploratory factor analysis and correlation techniques were employed to assess how well each item in the 3C index measured the respective con-structs (confidence, complacency, and convenience). Also, Cronbach's alpha was employed to ascertain the internal consistency and reliability of the items in each con-struct. An alpha value of 0.7 or higher was taken as acceptable reliability. Items with poor loadings on those constructs were dropped (see Appendix 1), the remaining items were indexed, and a summary score was obtained. Our primary outcome variable was COVID-19 vaccination status, while 3C model categorization and demographic characteristics were our independent variables. Simple frequencies and percentages were used to describe the distribution of respondents' socio-demographic characteristics and their willingness to accept the COVID-19 vaccine. Bivariate analysis was used to identify the association between the outcome and independent variables. For the multivariate analysis, a binary logistic regression model was built, and variables that were significant (p < 0.05) in the bivariate analysis were included in the model. For group comparisons, adjusted odds ratios with corresponding 95% confidence intervals were presented. All quantitative analysis was conducted using the open-source software R (version 4.1.1) in the R-Studio environment. Professional bilingual transcribers transcribed audio recordings from the qualitative interviews. All transcription then went through a quality check and familiarization by the research team. Thematic codes were then developed deductively and inductively before analysis. The analysis was conducted using Dedoose software. 3. Results 3.1. Descriptive and bivariate analysis of COVID-19 acceptance The distribution of respondents’ socio-demographic characteristics is shown in Table 1 . From the online survey distributed to the HCWs, a total of 710 responses were received across 28 states in Nigeria. Out of these, 86% (612) were respondents from the study setting, representing the six geo-political regions of the country (see Appendix 2). The mean age of the respondents is 37 years, with a standard deviation of 10 years. The study participants were divided into 3 age groups: youth (18–29 years), adults (30–49 years), and older adults (50 years and above). Most respondents were aged 30–49 years (61%). Fifty-nine percent of the respondents are female. The majority of the respondents are married (60%), followed by those who are single (33%). More than half are Christians (58%). One-third of the respondents are of Igbo ethnicity (32%). Community health workers (33%) and nurses (24%) make up more than half of the sample by job specialty. Furthermore, 62% of the respondents work in primary healthcare facilities. Seventy-nine of the sampled health facilities are publicly owned, and the majority of the facilities are located in urban areas (43%). 42 healthcare workers participated in the qualitative interviews; 60% were female Among the 592 respondents (see Table 1 ) that answered the question, “Have you received the COVID-19 vaccine?”, 86% have been vaccinated. The chi-squared test revealed a significant association between COVID-19 vaccination uptake and age (p < 0.01), marital status (p = 0.02), religion (p < 0.01), ethnicity (p < 0.01), facility type (p < 0.01), facility ownership (p < 0.01), facility location (p < 0.01), and job specialty (p < 0.01). Vaccination coverage across different specialties of healthcare workers reveals that community health workers and medical doctors reported over 90% vaccination rates compared to nurses (82%), public health officers (82%), lab technicians (76%), pharmacists (57%), and other specialties (89%). This finding is supported by the qualitative data where one respondent reported that doctors have a higher acceptance rate than other specialties because they have first-hand proof that the virus exists and experience with infected patients: “Uhm all, doctors believe in it most... Because they consult with people, they check the signs of the disease, how it is, then indicate it and send it to the lab....Lab workers, they are the ones that can collect blood samples and test them”. (FGD with CHWs at the PHC level) Table 1 Descriptive statistics and Bivariate analysis of COVID-19 vaccine acceptance Variable Total N(%) COVID-19 Vaccination (n = 592) P-value Not vaccinated n(%) Vaccinated n(%) Age 0.00 Mean (SD) 37 (10) 18–29 148 (24) 33 (23) 111 (77) 30–49 375 (61) 43 (12) 317 (88) 50+ 89 (15) 6 (7) 82 (93) Gender 0.36 Female 362 (59) 53 (15) 299 (85) Male 250 (41) 29 (12) 211 (88) Religion 0.00 Christian 354 (58) 51 (15) 290 (85) Muslim 230 (38) 19 (8) 207 (82) Prefer not to say 21 (3) 8 (44) 10 (56) Others 4 (1) 1 (25) 3 (75) Traditional 3 (0) 3 (100) 0 (0) Marital status 0.02 Married 367 (60) 39 (11) 316 (89) Single 204 (33) 36 (18) 162 (82) Widowed 21 (3) 2 (10) 18 (90) Prefer not to say 15 (2) 5 (33) 10 (67) Divorced 5 (1) 0 (0) 4 (100) Ethnicity 0.00 Igbo 194 (32) 34 (18) 155 (82) Hausa 165 (27) 8 (5) 155 (95) Others 132 (22) 15 (12) 113 (88) Yoruba 79 (13) 5 (7) 68 (93) Prefer not to say 42 (7) 20 (51) 19 (49) Job Specialty 0.00 Community Health Worker 202 (33) 14 (7) 185 (93) Nurse 149 (24) 26 (18) 117 (82) Others 82 (13) 9 (11) 70 (89) Public Health Officer 68 (11) 12 (18) 54 (82) Medical Doctor 51 (8) 4 (8) 47 (92) Lab Technician 36 (6) 8 (24) 25 (76) Pharmacist 24 (4) 9 (43) 12 (57) Health Facility Type 0.00 Primary 377 (62) 30 (8) 335 (92) Secondary 173 (28) 39 (23) 131 (77) Tertiary 62 (10) 13 (23) 44 (77) Health Facility Location 0.00 Urban 262 (43) 35 (14) 216 (86) Rural 227 (37) 20 (9) 200 (91) Semi-Urban 123 (20) 27 (22) 94 (78) Health Facility Ownership 0.00 Public health facility 486 (79) 33 (7) 443 (93) Private health facility 116 (19) 49 (45) 59 (55) Nonprofit health facility 10 (2) 0 (0) 8 (100) Total 612 82 (14) 510 (86) 3.2. Drivers of COVID-19 vaccine acceptance Table 2 shows the result of the multivariate logistic regression. Compared to younger healthcare workers, adults (adjOR = 3.53, 95% CI: 1.47–8.51, p = 0.01) and older healthcare workers (adjOR = 11.40, 95% CI: 2.27–57.17, p = 0.01) are more likely to accept the COVID-19 vaccine. The odds of accepting the COVID-19 vaccine are higher for healthcare workers who are Christians (adjOR = 5.71, 95% CI: 1.14–28.70, p = 0.04) and Muslims (adjOR = 13.25, 95% CI: 2.04–86.26, p = 0.01) when compared to those who chose non-disclosure. Also, community health workers (adjOR = 5.02, 95% CI: 1.46–17.26, p = 0.01) are more likely to accept vaccination than others. Compared to healthcare workers in Enugu (South East), healthcare workers from Kogi (North Central) are less likely (adjOR = 0.20, 95% CI: 0.06–0.64, p = 0.01) to be vaccinated. Healthcare workers who are confident in the COVID-19 vaccines are five times more likely to be vaccinated (adjOR = 5.34, 95% CI: 2.69–10.65, p < 0.01) compared to those who are not confident in the vaccine's safety and efficacy. Convenience in accessing the COVID-19 vaccine is highly associated with vaccine acceptance (adjOR = 7.72, 95% CI: 3.50–17.04, p < 0.01), when compared to healthcare workers who find it inconvenient to access the vaccine. Furthermore, healthcare workers who are not complacent about COVID-19 and the COVID-19 vaccine have higher odds (adjOR = 2.95, 95% CI: 1.33–6.55, p = 0.01) of being vaccinated compared to those who are complacent. Based on the preceding information, the 3Cs are significant predictors of vaccine acceptance, with convenience having the highest predictive value (7.7 odds ratio), followed by confidence (5.4 odds ratio), and complacency (2.9 odds ratio). From the qualitative interview, a vaccinated healthcare worker reported that his motivation for getting vaccinated was largely because the vaccines were available within his location. The place I went to get it was available around our vicinity. ( FGD with Nurse at Apapa General Hospital) Also, three unvaccinated healthcare workers responded when asked what will make them take the vaccine. “If the vaccines are brought to us instead of we having to close work and stress ourselves to go to look for where to get vaccinated, and if we can be assured of the safety of the vaccine itself. Yeah”. (KII HCP at Private Tertiary facility) what will make it easier is for me to go down to vaccinate in my facility, that’s what makes it easier for me to go and take it (KII HCP at private PHC) it is not available as at now in my facility. I know it is available in the country but to my own reach, it is not available for now. (KII HCP at private PHC) Table 2 Multivariate logistic result of socio-demographic characteristics and 3Cs associated with COVID-19 vaccine acceptance Variable Unadjusted ORs (95% CI) P value Adjusted ORs (95% CI) P value Age 18–29 ref ref 30–49 2.19 (1.33–3.62) 0.00 3.53 (1.46–8.51) 0.01 50+ 4.06 (1.63–10.15) 0.00 11.40 (2.27–57. 17) 0.00 Gender Female ref ref Male 1.29 (0.79–2.10) 0.31 2.01 (0.89–4.53) 0.09 Religion Prefer not to say ref ref Christian 4.55 (1.71–12.07) 0.00 5.71 (1.14–28.70) 0.04 Muslim 8.72 (3.08–24.70) 0.00 13.25 (2.04–86. 26) 0.01 Others 2.40 (0.21–27.72) 0.48 1. 08 ((0.05–21.74) 0.96 Marital status Prefer not to say ref ref Married 4.05 (1.32– 12.47) 0.02 2.00 (0.37–10.75) 0.42 Single 2.25 (0.73–6.98) 0.16 4.91 (0.81–29.69) 0.08 Widowed 4.50 (0.73–27.58) 0.10 5.52 (0.22–141.54) 0.30 Job Specialty Others ref ref Community Health Worker 1.70 (0.70–4.10) 0.24 5.02 (1.46–17.26) 0.01 Nurse 0.58 (0.26–1.31) 0.19 1.87 (0.620–5.67) 0.27 Public Health Officer 0.58 (0.23–1.47) 0.25 1.20 (0.35–4.13) 0.77 Medical Doctor 1.51 (0.44–5.19) 0.51 2.67 (0.58–12.44) 0.21 Lab Technician 0.40 (0.14–1.16) 0.09 1.12 (0.24–5.30) 0.89 Pharmacist 0.17 (0.06–0.52) 0.00 0.30 (0.06–1.40) 0.13 Region Enugu ref ref Gombe 1.50 (0.59–3.85) 0.40 2.06 (0.49–8.63) 0.33 Kogi 0.22 (0.10–0.45) 0.00 0.20 (0.06–0.64) 0.01 Lagos 1.00 (0.42–2.32) 0.98 1.79 (0.56–5.69) 0.33 Rivers 1.05 (0.44–2.52) 0.91 1.52 (0.49–4.65) 0.47 Kano 6.67 (1.45–30.64) 0.02 2.46 (0.34–17.79) 0.37 Confidence Not Confident ref ref Confident 7.17 (4.30–11.97) 0.00 5.36 (2.69–10.65) 0.00 Complacency Complacent ref ref Non-complacent 4.69 (2.81–7.84) 0.00 2.95 (1.33–6.55) 0.01 Convenience Not Convenient ref ref Convenient 16.47 (9.39–28.89) 0.00 7.72 (3.50–17.04) 0.00 Note: bold p-values are statistically significant at p < 0.05; ref = reference group 3.3. Perceived barriers to COVID-19 vaccination In an attempt to understand why 14% (82) of the healthcare workers have not been vaccinated, we explored the reasons for non-vaccination among the unvaccinated respondents. We adapted the World Health Organization BeSD framework (WHO, 2022) to assess and address behavioral and social drivers for low uptake. We analyzed the reasons for non-vaccination among the unvaccinated health workers using simple percentages and classified their responses into three domains of the BeSD framework. In Fig. 2, the chart reveals that vaccine safety and efficacy (58%) were the top reasons for non-vaccination. These reasons are related to the thinking and feeling behaviors of the respondents. Also, access to vaccines and a busy work schedule (16%) are other reasons for non-vaccination, which express practical barriers to vaccination. And lastly, decision autonomy (6%) proved to be another strong reason for non-vaccination relating to the social process domain. Respondents in the qualitative interview reported a lack of confidence in the efficacy and safety of the vaccine due to rumors circulating in various news outlets and on social media. Few expressed concerns that the vaccines were manufactured too quickly and lack of confidence in the vaccine manufacturers the efficacy, the safety of the vaccine, frankly speaking from all that I have read, from all I understand and ehn as a health worker who understand the process of vaccine production you know to me this vaccine came rather too quick… (FGD with Doctor) ...I am beginning to suspect that even the company that produces this vaccine, maybe the money they have sunk into producing this vaccine, they want to recoup that money, so they keep on promoting the idea of people taking this vaccine whether they like it or not… (FGD with Doctor) : 4. Discussion Our study assessed COVID-19 acceptance and hesitancy among healthcare workers in Nigeria. Our attempt was to find out the level of acceptance among the different cadres of health specialties that we have in Nigeria. In the analysis of various job specialties, it was observed that community health workers and doctors exhibited a higher acceptance rate compared to other specialties. It is worth noting that there is a scarcity of studies reporting a higher acceptance rate, specifically among community health workers. Our result could be attributed to several factors. For our study, community health workers make up the highest number of respondents. A justification for this is the way the Nigeria health system is structured, to have more primary health centers than others and community health workers are found here. Also, Community health workers’ close relationship with communities and the kind of training they receive in disease prevention and control contributes may to the high uptake of the Covid-19 vaccines. Community health workers are present within local communities, serving as the initial point of contact for individuals and playing a crucial role in encouraging healthy practices among community members. As part of their specialized training, they gain access to comprehensive knowledge about diseases and the significance of vaccines. Through this training and their responsibilities, community health workers develop a sense of assurance and confidence in receiving vaccinations. We also found that doctors, aside from community health workers, have a higher acceptance rate than other specialties. This finding is unique given the number of doctors that form the sample from the study. The high acceptance rate of doctors could be explained by the fact that as much as all health workers are at risk of being infected, doctors have a higher risk because of frequent exposure to infected persons and first-hand knowledge or information from patients about the virus. Also, Physicians' access to vaccine information, their education and knowledge of the vaccine development process, and their role in promoting the health of patients may be attributed to their higher acceptance of the Covid-19 vaccine. Our findings mirror other studies [ 24 , 25 ]. [ 24 ] reported that physicians have a high acceptance rate and higher odds of being tally vaccinated than other specialties in Nigeria. [ 25 ] study to access health worker vaccine acceptance in Ethiopia. He found that health workers other than doctors and nurses are more likely to be vaccine-hesitant. [ 26 ] found that medical health workers are pre-dispose to accept the Covid-19 vaccine because of their education and trust in the health system and their understanding of the importance of vaccines and the vaccine manufacturing process. We then adopted the multiple logistics regression of the 3C model to assess the probability of COVID-19 acceptance among health workers. From the analysis, we found that the 3C model is a significant predictor of COVID-19 vaccine acceptance among health workers. The analysis revealed that among the 3C predictors of confidence, convenience, and complacency, convivence was a bigger predictor of vaccine acceptance. A plausible reason for convenience is a bigger predictor of vaccine acceptance among health workers in Nigeria can be attributed to the busy schedule of most health workers. Health workers are often overworked. They are always faced with numerous work responsibilities that demand their time and attention, mostly in low-resourced settings. They always expected to work long hours seeing many patients, with additional administrative duties, leaving them with no time to get vaccination, except the facility is a vaccination center. Also, there is little or no flexibility in the work schedule of most health workers in Nigeria, making it challenging for professional health workers to find a convenient time to visit vaccination centers, which work mostly during regular working hours. in their study, Ashok et al. (2021) revealed that if health workers are offered convenience-based incentives such as prices and transportation, they will pre-dispose to accepting the Covid-19 vaccine. In our attempt to understand the barriers to COVID-19 vaccination among health workers, we adapted the WHO’s Behavioural and Social Drivers (BeSD) of vaccination framework to examine the barriers to COVID-19 vaccination among health workers. Using simple percentages to classify health workers’ responses according to 3 domains of the BeSD framework (thinking and feeling behavior, practical barriers and social process domain), we found responses around the thinking and feeling domain were the main barriers to COVID-19 vaccination among health workers. These responses relate mostly to issues around vaccine safety and efficacy. Health workers in Nigeria as expected, have access to more information about vaccine production and vaccine adverse effects. As studies such as that of [ 27 , 28 , 29 ] have shown, there are a lot of misinformation and conspiracy theories about the efficacy, effectiveness adverse effects of Covid-19 vaccines available in the country. These information and conspiracy theories are mostly distributed by trusted sources. This information creates skepticism causing health workers to be hesitant about taking the vaccines, especially in communities. There are also concerns about the relatively short period used in developing the Covid-19 vaccine. The urgent need to curb the spread of the COVID-19 pandemic necessitated accelerating the timeline for the production of COVID-19 vaccines compared to traditional vaccine development. This brought concerns about the efficacy, effectiveness, and long-term effect of the COVID-19 vaccine developed, especially among health professionals, leading to hesitancy. In Latvia, [ 30 ], found that concerns around vaccine safety and efficacy were the main barriers among health workers. [ 31 ] found concerns around vaccine safety, efficacy and the potential side effect of the COVID-19 vaccines were the reasons for vaccine hesitancy among health workers. 5. Strength and Limitation For strength, the adoption of the mixed method design for our study offers several advantages, such as improving the quality and comprehensiveness of the research outcome. By triangulating data from both qualitative and quantitative responses, the study is able to establish a more robust and comprehensive understanding of COVID-19 vaccine behaviour of health workers. triangulation of the different data sources enhances the validity of our findings and allows us to situate the findings with the socio-economic, cultural and healthcare settings of Nigeria. Also, The timing of our study gives room for a better insight to design strategies and interventions that will help conduct a mop-up exercise for vaccinated health workers in Nigeria. By conducting this study at this time and the finding from it, we are able to get a first-hand understanding of the specific barriers to vaccination among health workers in Nigeria. With this understanding, a tailored and specific intervention can be designed to reach the unvaccinated population of health workers in Nigeria. For the limitations, Although the data for this study was collected from six states to represent the six geographical regions in Nigeria. But the behavioral characteristics of the people from the selected states are most times different from those of the other states in the same region. Hence, the data collected from the participants from the selected states may not represent that of the entire population of health workers due to sampling and self-reporting. Participants for this study were self-selected for this, which may have caused some biases based on sample selection, hereby limiting the generalization of the result to all health workers. Also, health workers who volunteered or are self-selected to be part of this study might have vaccines, thereby creating a bias towards other health workers who may have other views about the vaccines. This can cause the data collected to be skewed. Another point to note is that participants who self-select into studies may provide responses that are favorable and acceptable, making results show favorable. 6. Implication Our findings revealed a higher acceptance rate of COVID-19 vaccine for CHWs and physicians when compared to their specialties. To bridge the gap between specialties, there is a need for efforts or interventions that encourages peer-to-peer mentoring and coaching or collaborative learning among the different health specialization, where positive experiences and learnings from vaccination can be shared. We also found that convenience was the primary predictor of vaccine behavior among health workers when testing against 3C variables. There is a need for policy and programmatic efforts toward convenient access to vaccination services for health workers in Nigeria. This can be done by establishing vaccination centers where they are easily accessible for all health workers, such as in their health establishments, making the vaccination schedule flexible to allow for vaccination at any time and reducing administrative barriers for health workers that is associated with vaccination, this includes reducing vaccination waiting time, as well as reducing paperwork. We adopted the BeSD framework to understand barriers to vaccination among health workers in Nigeria. We found that issues around the thinking and feeling domain have the main barriers to vaccination among health workers. This calls for campaigns that focus on addressing vaccine infodemics and conspiracy theories around vaccines. To do this, there is a need for focused educational campaigns that will provide credible and verified information about the efficacy and safety of vaccines available in Nigeria. Attempts should be made to address the concern of health workers around the development of the vaccines. Finally, efforts should also be made to build the confidence of health workers around the COVID-19 vaccines. These can be achieved by providing fact-based and research-domain information that addresses doubts amongst health workers. As at the time of this study, it is mostly public health facilities that are designated COVID-19 vaccination centres in Nigeria. There is a need to address this disparity which makes it difficult for most health workers to get vaccinated. There is need to improve on the distribution channels of the COVID-19 vaccination program in Nigeria. This can be achieved by establishing a partnership that involves public health authorities and the private sector to enable private health establishments to administer the COVID-19 vaccine. This partnership will enable more health facilities offer COVID-19 vaccination scenes, thereby increasing accessibility especially to health workers. Declarations Institutional Review Board Statement: All necessary research ethical process was met for this study. We obtained ethical approval from John Hopkins University International Review Board (Approval Number: IRB00017765). In Nigeria, ethical approval was obtained from the National Health Research Ethics Committee of Nigeria (NHREC/01/01/2007-27/10/2021). in all the selected states, except Lagos, where ethical approval was weived, we were granted approved by the relevant committees; Kano (NHREC/17/03/2018), Gombe (MOH/ADM/621/Vol.1/385), Kogi (MOH/PRS/465/V.1/018), Enugu (MH/MSD/REC21/244), and Rivers (RSUTH/REC/2021128). Informed Consent Statement: We used both written and oral methods to obtain consent from the respondents for the qualitative and quantitative interviews. We used standard oral consent to obtain permission from each respondent. For all interviews, we detailed the study’s objective and assured them of the confidentiality of all information provided. We also provided phone credit one thousand naira as incentives to offset data costs for the quantitative interviews conducted. Funding declaration: This study was funded by Johnson & Johnson, with funding number 990095573-9100000000-138510. Funding acquisition was by Chizoba Wonodi. The funder had no influence on the analysis or interpretation of the findings presented in this manuscript. Clinical trial number: not applicable. Data Availability: The dataset generated and analyzed during the study are not publicly available due to privacy and ethical restrictions but will be provide upon reasonable request by the corresponding author. Conflicts of Interest: authors declare no conflict of interest Author Contributions: Conceptualization, C.B.W., P.U.A., I. A.O., A.A.A., and S.N.; Methodology, P.U.A., I.A.O., and A.A.A.; validation, C.B.W., and S.N.; Formal analysis, I. A.O., P.U.A., and A.A.A.; investigation, C.B.W., P.U.A., I. A.O., A.A.A., and S.N.; resources, C.B.W., and S.N.; writing—original draft preparation P.U.A., I.A.O., and A.A.A.; writing—review and editing, C.B.W., P.U.A., I. A.O., A.A.A., and S.N.; visualization, P.U.A., I.A.O., and A.A.A.; project administration, C.B.W., and S.N.; funding acquisition C.B.W., and S.N.. References Babatope T, Ilyenkova V, Marais D. COVID-19 vaccine hesitancy: a systematic review of barriers to the uptake of COVID-19 vaccine among adults in Nigeria. Bul Nat Res Cen. 2023;47(1):45. Olu-Abiodun O, Abiodun O, Okafor N. COVID-19 vaccination in Nigeria: A rapid review of vaccine acceptance rate and the associated factors. PLoS ONE. 2022, 17(5). World Health Organization (WHO)‎. Behavioral and social drivers of vaccination: tools and practical guidance for achieving high uptake. World Health Organ. 2022 https://apps.who.int/iris/handle/10665/354459 Africa CDC. COVID-19 Vaccination: Latest updates from Africa CDC on progress made in COVID-19 vaccinations on the continent. 2022. Nguyen, L. H.; Drew, D. A.; Joshi, A. D.; Guo, C. G.; Ma, W.; Mehta, R. S.; Sikavi,D. R.; Lo, C. H.; Kwon, S.; Song, M.; Mucci, L. A.; Stampfer, M. J.; Willett, W. C.;Eliassen, A. H.; Hart, J. E.; Chavarro, J. E.; Rich-Edwards, J. W.; Davies, R.; Capdevila,J.; Lee, K. A.; … Chan, A. T. Risk of COVID-19 among frontline healthcare workers and the general community: a prospective cohort study. medRxiv: the preprint server for health sciences, 2020,.04.29.20084111. Jung J, Kang SW, Lee S, Park H, Kim JY, Kim SK, Park S, Lim YJ, Kim EO, Lim SY, Chang E, Bae S, Kim MJ, Chong YP, Lee SO, Choi SH, Kim YS, Park MS, Kim SH. Risk of transmission of COVID-19 from healthcare workers returning to work after a 5-day isolation, and kinetics of shedding of viable SARS-CoV-2 variant B.1.1.529 (Omicron). J Hosp Inf. 2023;131:228–33. National Primary Health Care Development Agency (NPHCDA). 2021. Accessed on Feb. 21, 2023. https://web.facebook.com/NPHCDA/?_rdc=1&_rdr World Health Organization. (‎2022)‎. Verified Vaccine Information. The current COVID-19 situation. Available online: https://www.who.int/countries/nga/ Accessed 12 November 2022. Amuzie CI, Odini F, Kalu KU, Izuka M, Nwamoh U, Emma-Ukaegbu U, Onyike G. Covid-19 vaccine hesitancy among healthcare workers and its socio-demographic determinants in Abia State, south-east Nigeria: A cross-sectional study. Pan Afri Med J. 2021, 40. Nnaemeka VC, Onwe RO, Ekwedike AL, Oyedele OE, Tsiterimam TS, Ochepo OE, Nwokoye NN, Ike AC. Predictors of COVID-19 Vaccine Acceptance among Healthcare Workers in Nigeria. Vaccines. 2022;10(10):1645. Moucheraud C, Phiri K, Whitehead HS, Songo J, Lungu E, Chikuse E, Phiri S, Van Oosterhout JJ, Hoffman RM. Uptake of the COVID-19 vaccine among healthcare workers in Malawi. Int Health. 2022;15(1):77–84. Asumah MN, Abubakari A, Fosu B, Dzantor EK, Agyapong PD, Harrison SB, Apio G, Abukari A. Determinants of COVID-19 vaccine acceptance and hesitancy among healthcare professionals in the Kintampo North Municipality, Bono East Region, Ghana. Gh Med J. 2022;56(3):152–9. https://doi.org/10.4314/gmj.v56i3.4 . Moltot T, Lemma T, Silesh M, Sisay M, Shewangizaw A, Getaneh T, Tsegaw B. COVID-19 vaccine acceptance among health care professionals in Ethiopia: A systematic review and meta-analysis. Hum Vac imm. 2023;19(1):2188854. Nomhwange T, Wariri O, Nkereuwem E, Olanrewaju S, Nwosu N, Adamu U, Danjuma E, Onuaguluchi N, Enegela J, Nomhwange E, Jean Baptiste AE, Mulombo WK. Covid-19 vaccine hesitancy amongst Healthcare Workers: An assessment of its magnitude and determinants during the initial phase of National Vaccine Deployment in Nigeria. EClinicalMedicine. 2023;50:101499. Ojewale LY, Mukumbang FC. COVID-19 vaccine hesitancy among Nigerians living with non-communicable diseases: a qualitative study. BMJ Open 2023, 13(2), e065901. Robinson ED, Wilson P, Eleki B, Wonodi W. Knowledge, acceptance, and hesitancy of COVID-19 vaccine among health care workers in Nigeria. MGM J Med Sci. 2021;8(2):102. World Health Organization. Report of the SAGE working group on Vaccine Hesitancy. 2014. https://cdn.who.int/media/docs/default-source/immunization/demand/summary-of-sage-vaccinehesitancy-en.pdf?sfvrsn=abbfd5c8_2 . Accessed 13 Feb. 2023. World Health Organization. Behavioural and social drivers of vaccination: tools and practical guidance for achieving high uptake. World Health Organization. 2022, https://apps.who.int/iris/handle/10665/354459 . Accessed 13 Feb. 2023. Tostrud L, Thelen J, Palatnik A. Models of determinants of COVID-19 vaccine hesitancy in non-pregnant and pregnant population: Review of current literature. Hum Vac Imm. 2022, 18(6). Alagarsamy S, Mehrolia S, Pushparaj U, S J. Explaining the intention to uptake COVID-19 vaccination using the behavioral and social drivers of vaccination (BeSD) model. Vaccine: X. 2022;10:100140. https://doi.org/10.1016/j.jvacx.2021.100140 . Gerretsen P, Kim J, Caravaggio F, Quilty LC, Sanches M, Wells S, Brown ED, Agic B, Pollock BG, Graff-Guerrero A. Individual determinants of COVID-19 vaccine hesitancy. PLoS ONE. 2021 16(11), e0258462. MacDonald NE. Vaccine hesitancy: Definition, scope and determinants. Vaccine. 2015;33(34):4161–4. Kelley K, Maxwell SE. Sample Size for Multiple Regression: Obtaining Regression Coefficients That Are Accurate, Not Simply Significant. Psy Meth. 2003;8(3):305–21. Agha S, Chine A, Lalika M, Pandey S, Seth A, Wiyeh A, Seng A, Rao N, Badshah A. Drivers of COVID-19 Vaccine Uptake amongst Healthcare Workers (HCWs) in Nigeria. Vaccines. 2021;9(10):1162. Mohammed R, Nguse TM, Habte BM, Fentie AM, Gebretekle GB. Covid-19 vaccine hesitancy among Ethiopian Healthcare Workers. PLoS ONE 2021, 16(12). Adejimi AA, Odugbemi BA, Odukoya OO, Okunade KS, Taiwo AO, Osibogun A. Volunteering during the COVID-19 pandemic: Attitudes and perceptions of clinical medical and dental students in Lagos, Nigeria. Nig Pg Med J. 2021;28(1):1. Wonodi C, Obi-Jeff C, Adewumi F, Keluo-Udeke SC, Gur-Arie R, Krubiner C, Faden R. Conspiracy theories and misinformation about COVID-19 in Nigeria: Implications for vaccine demand generation communications. Vaccine. 2022;40(13):2114–21. Olatunji OS, Ayandele O, Ashirudeen D, Olaniru OS. Infodemic in a pandemic: COVID-19 conspiracy theories in an African country. Soc Health Beh. 2020;3(4):152. Oyeyemi SO, Fagbemi S, Busari II, Wynn R. Health workers’ beliefs in COVID-19 conspiracy theories, level of trust in government information and their willingness to take COVID-19 vaccines: A survey from Nigeria (Preprint). JMIR Form Res 2023 7, e41925. Lielsvagere-Endele S, Kolesnikova J, Puzanova E, Timofejeva S, Millere I. Motivators and barriers to COVID-19 vaccination of healthcare workers in Latvia. Fron Psy. 2022, 13. Biswas N, MustMoucheraud C, Phiri K, Whitehead HS, Songo J, Lungu E, Chikuse E, Phiri S, Van Oosterhout JJ, Hoffman RM. Uptake of the COVID-19 vaccine among healthcare workers in Malawi. Int Health. 2022;15(1):77–84. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":396261,"visible":true,"origin":"","legend":"\u003cp\u003eReasons for non-vaccination among the unvaccinated respondents\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7696059/v1/b8455592d5bee003320b673b.jpeg"},{"id":101847290,"identity":"c1e1f156-4074-4683-a108-9ff3005243c9","added_by":"auto","created_at":"2026-02-04 09:28:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1577510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7696059/v1/6342cc87-6cfd-4a8f-b4f3-fccce9939818.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"COVID-19 Vaccine Acceptance and Hesitancy Among Healthcare Workers in Nigeria. A Mixed-Method Analysis Using the WHO 3C and BeSD Model","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eNigeria has made substantial gains in ensuring that its population has unrestricted access to the COVID-19 vaccines. But convincing the eligible adult population to accept the COVID-19 vaccine and achieving its set target of vaccinating 70% of the population by the end of 2022 has become a complex and multifaceted challenge for the government and its partners \u003cstrong\u003e[1, 2].\u003c/strong\u003e As of November 2022, Nigeria had received over one hundred million doses of viable vaccines, with more than 50% already administered \u003cstrong\u003e[3, 4].\u003c/strong\u003e COVID-19 vaccination in the country is still lagging, particularly among health workers. As frontline responders, health workers have been the unsung heroes in the fight against the spread of the COVID-19 virus. As first responders, they are exposed to the virus due to their direct contact with and proximity to infected individuals, making them a potential means for transmission to the wider community \u003cstrong\u003e[5, 6].\u003c/strong\u003e Healthcare workers\u0026rsquo; potential risk to the wider community underscores the need for all health workers to be vaccinated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailable data revealed that the Nigerian government and its agency, the NPHCDA, planned to vaccinate 2,114,933 healthcare workers by the end of 2021 \u003cstrong\u003e[7]. \u0026nbsp;\u003c/strong\u003eBut according to the \u003cstrong\u003e[8],\u003c/strong\u003e as of November 2022, Nigeria had only vaccinated 25% of its health workforce. The study by \u003cstrong\u003e[9],\u003c/strong\u003e that collected data from 422 healthcare workers in Abia State revealed that only 49.5% had received the vaccine. \u003cstrong\u003e[10]\u003c/strong\u003e study in the Northern, Western, and Eastern regions that collected data from 710 healthcare workers revealed that only 59.3% had received the COVID-19 vaccine. These data show that there is a gap between the actual target and the number of health workers who have been vaccinated and also that there is variability in the coverage estimates among health workers who have received the COVID\u0026ndash;19 vaccine, which can be attributed to the population studied or the sampling procedure adopted. The data also shows that hesitancy among health workers is a problem in Nigeria. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvidence from previous studies also shows that Nigeria still lags behind other African countries in terms of vaccinating health workers. \u003cstrong\u003e[11]\u003c/strong\u003e found that 82.5 percent of health workers in Malawi have received at least the first dose of the COVID-19 vaccine. Another study in Ghana found that 78.6 percent of health workers have received the COVID-19 vaccine \u003cstrong\u003e[12].\u0026nbsp;\u003c/strong\u003eIn Ethiopia, \u003cstrong\u003e[13],\u003c/strong\u003e in a systematic literature review, found that 54.6% of healthcare workers have been vaccinated. The Nigerian government and partners have implemented various measures to ensure that frontline workers are vaccinated. These measures include a mandate for all civil servants, including health workers, to be vaccinated, training programs, and recurring sensitization programs on the importance of vaccination for healthcare providers. Despite these efforts, the problem of hesitancy among healthcare workers persists. Reported vaccine hesitancy \u0026nbsp;was around vaccine safety and negative information paddled around by the media \u003cstrong\u003e[14\u003c/strong\u003e], mistrust of government and vaccine manufacturer intentions, vaccine infodemics, and fear of harmful psychological consequences \u003cstrong\u003e[15],\u003c/strong\u003e issues around vaccine side effects, and the fear of the unknown \u003cstrong\u003e[16\u003c/strong\u003e].\u003c/p\u003e\n\u003cp\u003eLiterature has provided evidence of studies assessing drivers of acceptance and barriers to COVID-19 vaccination among healthcare workers in Nigeria. None have applied the 3C or behavioral and social drivers (BeSD) model in Nigeria. The 3C and BeSD models are two frameworks developed by the World Health Organization (WHO) that have been applied to understanding people\u0026rsquo;s vaccination behavior as well as to developing tools to measure social and behavioral drivers of vaccination uptake \u003cstrong\u003e[17-19]\u003c/strong\u003e. The 3C model highlights 3 key factors that drive vaccination behaviour; (i) complacency, which refers to the perceived need for the vaccine; (ii) convenience, which refers to the ease of accessing the vaccines; and (iii) confidence, which refers to the trust in the vaccine\u0026apos;s safety and efficacy \u003cstrong\u003e[17]\u003c/strong\u003e. The BeSD model identifies four key domains that individual, interpersonal, and social factors can be grouped into to understand the complex interplay of factors that influence vaccination behaviour; (i) thinking and feeling about vaccination; (ii) social processes that drive or inhibit vaccine behavior; (iii) motivation that drives or inhibits vaccine seeking behavior; and (iv) practical issues involved in seeking and receiving vaccination \u003cstrong\u003e[18, 20]\u003c/strong\u003e. Development partners and scholars have recently acknowledged the importance of the 3C and BeSD models as being readily understandable and able to be used to answer research questions that guide the development of intervention and risk communication strategies to improve vaccine uptake and address vaccine hesitancy \u003cstrong\u003e[21, 22].\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address the knowledge gap in the literature regarding the application of 3C and BeSD models in analyzing COVID-19 vaccination in Nigeria, as well as using an updated survey representative of the healthcare worker population, this study aims to answer the following questions: Do acceptance and hesitancy of COVID-19 vaccines vary among healthcare workers in different specialties in Nigeria? What factors drive COVID-19 vaccination among healthcare workers in Nigeria? What barriers do healthcare workers perceive in relation to vaccination in Nigeria?\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study design\u003c/h2\u003e\u003cp\u003eThis is part of a larger cross-country study in three Sub-Saharan countries of Ethiopia, Kenya, and Nigeria, that adopted a mixed method approach to collect data from policymakers, healthcare workers, and clients to understand their journey to COVID-19 vaccination. For this part of the study, we adopted a mixed method approach that employed the depth of qualitative data collected using In-depth Inter-views (IDIs) and Focus Group Discussions (FGD) to triangulate data from quantitative survey interviews.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Study setting\u003c/h2\u003e\u003cp\u003eThe study was conducted in six randomly selected states from the six geopolitical zones that make up Nigeria. The six selected states represented states with low, medium, and high burdens of the COVID-19 infection rate in the country. The selected states are Enugu in the South-East, Rivers in the South-South, Lagos in the South-West, Kogi in the North-Central, Gombe in the North-East, and Kano in the North-West.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Sampling Procedure\u003c/h2\u003e\u003cp\u003eThe target population for this part of the study are public and private health workers drawn from the three levels of the health care system in Nigeria. The three tiers are primary, secondary, and tertiary health systems.\u003c/p\u003e\u003cp\u003eTo arrive at the quantitative survey's sample size, Kelley and Maxwell's power analysis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], was used to arrive at a precise population estimate of 316 at a 95% confidence level. The sample size was then inflated further by 67% to 527, rounded up to 600 to have a more representative sample of the population of health workers.\u003c/p\u003e\u003cp\u003eStructured convenience sampling were used to select the health facilities. We acquired a list of all health facilities in the selected states from the Ministry of Health registry. These formed our sampling frame. We then matched the national ratio of primary, secondary, and tertiary health facilities in each state. This gave us a ratio of twelve (12) primary healthcare facilities, six (6) secondary and two (2) tertiary health facilities per state. Within each selected health facility, respondents were sampled using snowball sampling technique. This we did by first deploying trained data collectors to distribute the instrument using online means at each selected health facility. The link to the instrument was given to the head of each selected health facility, who distributed it to their colleague, with specific instruction to distribute to others in the facility. For each healthcare worker that was sent the online survey instrument, instruction was also given to distribute the survey instrument to eligible colleagues. This process was repeated until the sample size was reached.\u003c/p\u003e\u003cp\u003eFor the qualitative samples, convenient sampling was used to select 42 healthcare workers from the three (3) tiers of the health system across 6 states that represented the six geopolitical regions of Nigeria. We conducted 18 KII with health workers and 3 FGDs at the selected health system. Each FGD had 8 participants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Instruments\u003c/h2\u003e\u003cp\u003eWe used survey questionnaire and key informant interview and focus group discussion guide to collect data from the selected states. The tools were designed to elicit information on vaccination intention and motivation, vaccination training and advocacy, vaccination sentiment, convenience, confidence and complacency.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Data collection procedure\u003c/h2\u003e\u003cp\u003eWe deployed the developed quantitative data collection tool into the online soft-ware Qualtrics, an integrated survey software that allows for easy survey creation, collection and analysis. The use of Qualtrics was out of the belief that a large percentage of healthcare workers are literate and have access to mobile internet. The survey link was first pre-tested in health facilities that were not part of the originally selected facility to check the validity of the survey questions. We then employed trained and experienced data collectors from the six states to distribute the online survey tool to the selected facilities. Between February 8 and March 30, 2022, data collectors distributed the link to each health facility. A minimum of five healthcare workers participated in the online survey from each health facility. The technical team provides real-time data monitoring as they come in and notify the health facility or data collectors once the state reaches the sample size.\u003c/p\u003e\u003cp\u003eQualitative interview guides designed by the research team to address the re-search question was first pre-tested by trained data collectors on healthcare workers in health facilities that are not part of the originally selected health facilities. Comments and feedback from the pre-test was then used to adjust the tool before commencing data collection. Across the six states, between February 10 and 18, 2022, 42 healthcare workers were interviewed. The interviews were conducted in both English and local Hausa, Yoruba and Igala dialects, lasting a minimum of 35 minutes. During each interview, with the consent of the respondents, we recorded all discussion and also took down notes to accurately capture information that is exchanged during the discussion.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Data analysis\u003c/h2\u003e\u003cp\u003eResponses from the online survey were cleaned using an Excel spreadsheet. All respondents who gave no consent, reported not being a health worker, or had incomplete responses were excluded from the analysis. We categorized responses according to the 3C model questions. Exploratory factor analysis and correlation techniques were employed to assess how well each item in the 3C index measured the respective con-structs (confidence, complacency, and convenience). Also, Cronbach's alpha was employed to ascertain the internal consistency and reliability of the items in each con-struct. An alpha value of 0.7 or higher was taken as acceptable reliability. Items with poor loadings on those constructs were dropped (see Appendix 1), the remaining items were indexed, and a summary score was obtained. Our primary outcome variable was COVID-19 vaccination status, while 3C model categorization and demographic characteristics were our independent variables. Simple frequencies and percentages were used to describe the distribution of respondents' socio-demographic characteristics and their willingness to accept the COVID-19 vaccine. Bivariate analysis was used to identify the association between the outcome and independent variables. For the multivariate analysis, a binary logistic regression model was built, and variables that were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the bivariate analysis were included in the model. For group comparisons, adjusted odds ratios with corresponding 95% confidence intervals were presented. All quantitative analysis was conducted using the open-source software R (version 4.1.1) in the R-Studio environment.\u003c/p\u003e\u003cp\u003eProfessional bilingual transcribers transcribed audio recordings from the qualitative interviews. All transcription then went through a quality check and familiarization by the research team. Thematic codes were then developed deductively and inductively before analysis. The analysis was conducted using Dedoose software.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. \u003cb\u003eDescriptive and bivariate analysis of COVID-19 acceptance\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe distribution of respondents\u0026rsquo; socio-demographic characteristics is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. From the online survey distributed to the HCWs, a total of 710 responses were received across 28 states in Nigeria. Out of these, 86% (612) were respondents from the study setting, representing the six geo-political regions of the country (see Appendix 2). The mean age of the respondents is 37 years, with a standard deviation of 10 years. The study participants were divided into 3 age groups: youth (18\u0026ndash;29 years), adults (30\u0026ndash;49 years), and older adults (50 years and above). Most respondents were aged 30\u0026ndash;49 years (61%). Fifty-nine percent of the respondents are female. The majority of the respondents are married (60%), followed by those who are single (33%). More than half are Christians (58%). One-third of the respondents are of Igbo ethnicity (32%). Community health workers (33%) and nurses (24%) make up more than half of the sample by job specialty. Furthermore, 62% of the respondents work in primary healthcare facilities. Seventy-nine of the sampled health facilities are publicly owned, and the majority of the facilities are located in urban areas (43%). 42 healthcare workers participated in the qualitative interviews; 60% were female\u003c/p\u003e\u003cp\u003eAmong the 592 respondents (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that answered the question, \u0026ldquo;Have you received the COVID-19 vaccine?\u0026rdquo;, 86% have been vaccinated. The chi-squared test revealed a significant association between COVID-19 vaccination uptake and age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), marital status (p\u0026thinsp;=\u0026thinsp;0.02), religion (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), ethnicity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), facility type (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), facility ownership (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), facility location (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and job specialty (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Vaccination coverage across different specialties of healthcare workers reveals that community health workers and medical doctors reported over 90% vaccination rates compared to nurses (82%), public health officers (82%), lab technicians (76%), pharmacists (57%), and other specialties (89%).\u003c/p\u003e\u003cp\u003eThis finding is supported by the qualitative data where one respondent reported that doctors have a higher acceptance rate than other specialties because they have first-hand proof that the virus exists and experience with infected patients:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Uhm all, doctors believe in it most... Because they consult with people, they check the signs of the disease, how it is, then indicate it and send it to the lab....Lab workers, they are the ones that can collect blood samples and test them\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(FGD with CHWs at the PHC level)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics and Bivariate analysis of COVID-19 vaccine acceptance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal N(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCOVID-19 Vaccination (n\u0026thinsp;=\u0026thinsp;592)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot vaccinated\u003c/p\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVaccinated\u003c/p\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e148 (24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111 (77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e375 (61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e317 (88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e362 (59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e299 (85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e250 (41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e211 (88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChristian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e354 (58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e290 (85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e230 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e207 (82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrefer not to say\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraditional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e367 (60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e316 (89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e204 (33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e162 (82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrefer not to say\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgbo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194 (32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e155 (82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHausa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e165 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e155 (95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYoruba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrefer not to say\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJob Specialty\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommunity Health Worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e202 (33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e185 (93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNurse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149 (24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e117 (82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70 (89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic Health Officer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Doctor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLab Technician\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePharmacist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth Facility Type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e377 (62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e335 (92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173 (28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131 (77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth Facility Location\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e262 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e216 (86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e227 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e200 (91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSemi-Urban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e123 (20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94 (78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth Facility Ownership\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic health facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e486 (79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e443 (93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate health facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49 (45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNonprofit health facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e612\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e82 (14)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e510 (86)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. \u003cb\u003eDrivers of COVID-19 vaccine acceptance\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the result of the multivariate logistic regression. Compared to younger healthcare workers, adults (adjOR\u0026thinsp;=\u0026thinsp;3.53, 95% CI: 1.47\u0026ndash;8.51, p\u0026thinsp;=\u0026thinsp;0.01) and older healthcare workers (adjOR\u0026thinsp;=\u0026thinsp;11.40, 95% CI: 2.27\u0026ndash;57.17, p\u0026thinsp;=\u0026thinsp;0.01) are more likely to accept the COVID-19 vaccine. The odds of accepting the COVID-19 vaccine are higher for healthcare workers who are Christians (adjOR\u0026thinsp;=\u0026thinsp;5.71, 95% CI: 1.14\u0026ndash;28.70, p\u0026thinsp;=\u0026thinsp;0.04) and Muslims (adjOR\u0026thinsp;=\u0026thinsp;13.25, 95% CI: 2.04\u0026ndash;86.26, p\u0026thinsp;=\u0026thinsp;0.01) when compared to those who chose non-disclosure. Also, community health workers (adjOR\u0026thinsp;=\u0026thinsp;5.02, 95% CI: 1.46\u0026ndash;17.26, p\u0026thinsp;=\u0026thinsp;0.01) are more likely to accept vaccination than others. Compared to healthcare workers in Enugu (South East), healthcare workers from Kogi (North Central) are less likely (adjOR\u0026thinsp;=\u0026thinsp;0.20, 95% CI: 0.06\u0026ndash;0.64, p\u0026thinsp;=\u0026thinsp;0.01) to be vaccinated. Healthcare workers who are confident in the COVID-19 vaccines are five times more likely to be vaccinated (adjOR\u0026thinsp;=\u0026thinsp;5.34, 95% CI: 2.69\u0026ndash;10.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) compared to those who are not confident in the vaccine's safety and efficacy. Convenience in accessing the COVID-19 vaccine is highly associated with vaccine acceptance (adjOR\u0026thinsp;=\u0026thinsp;7.72, 95% CI: 3.50\u0026ndash;17.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), when compared to healthcare workers who find it inconvenient to access the vaccine. Furthermore, healthcare workers who are not complacent about COVID-19 and the COVID-19 vaccine have higher odds (adjOR\u0026thinsp;=\u0026thinsp;2.95, 95% CI: 1.33\u0026ndash;6.55, p\u0026thinsp;=\u0026thinsp;0.01) of being vaccinated compared to those who are complacent. Based on the preceding information, the 3Cs are significant predictors of vaccine acceptance, with convenience having the highest predictive value (7.7 odds ratio), followed by confidence (5.4 odds ratio), and complacency (2.9 odds ratio).\u003c/p\u003e\u003cp\u003eFrom the qualitative interview, a vaccinated healthcare worker reported that his motivation for getting vaccinated was largely because the vaccines were available within his location.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe place I went to get it was available around our vicinity.\u003c/p\u003e\u003cp\u003e\u003cb\u003e( FGD with Nurse at Apapa General Hospital)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlso, three unvaccinated healthcare workers responded when asked what will make them take the vaccine.\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;If the vaccines are brought to us instead of we having to close work and stress ourselves to go to look for where to get vaccinated, and if we can be assured of the safety of the vaccine itself. Yeah\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(KII HCP at Private Tertiary facility)\u003c/b\u003e\u003c/p\u003e\u003cp\u003ewhat will make it easier is for me to go down to vaccinate in my facility, that\u0026rsquo;s what makes it easier for me to go and take it\u003c/p\u003e\u003cp\u003e\u003cb\u003e(KII HCP at private PHC)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eit is not available as at now in my facility. I know it is available in the country but to my own reach, it is not available for now.\u003c/p\u003e\u003cp\u003e\u003cb\u003e(KII HCP at private PHC)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic result of socio-demographic characteristics and 3Cs associated with COVID-19 vaccine acceptance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnadjusted ORs\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdjusted ORs\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.19 (1.33\u0026ndash;3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.53 (1.46\u0026ndash;8.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.06 (1.63\u0026ndash;10.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.40 (2.27\u0026ndash;57. 17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.29 (0.79\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.01 (0.89\u0026ndash;4.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrefer not to say\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChristian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.55 (1.71\u0026ndash;12.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.71 (1.14\u0026ndash;28.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.72 (3.08\u0026ndash;24.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.25 (2.04\u0026ndash;86. 26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.40 (0.21\u0026ndash;27.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1. 08 ((0.05\u0026ndash;21.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrefer not to say\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.05 (1.32\u0026ndash; 12.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00 (0.37\u0026ndash;10.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.25 (0.73\u0026ndash;6.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.91 (0.81\u0026ndash;29.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.50 (0.73\u0026ndash;27.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.52 (0.22\u0026ndash;141.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJob Specialty\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommunity Health Worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.70 (0.70\u0026ndash;4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.02 (1.46\u0026ndash;17.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNurse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.58 (0.26\u0026ndash;1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.87 (0.620\u0026ndash;5.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic Health Officer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.58 (0.23\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 (0.35\u0026ndash;4.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Doctor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.51 (0.44\u0026ndash;5.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.67 (0.58\u0026ndash;12.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLab Technician\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.40 (0.14\u0026ndash;1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12 (0.24\u0026ndash;5.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePharmacist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.17 (0.06\u0026ndash;0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30 (0.06\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnugu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGombe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.50 (0.59\u0026ndash;3.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.06 (0.49\u0026ndash;8.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKogi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.22 (0.10\u0026ndash;0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.20 (0.06\u0026ndash;0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLagos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.42\u0026ndash;2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79 (0.56\u0026ndash;5.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRivers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.05 (0.44\u0026ndash;2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.52 (0.49\u0026ndash;4.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.67 (1.45\u0026ndash;30.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.46 (0.34\u0026ndash;17.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConfidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Confident\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConfident\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.17 (4.30\u0026ndash;11.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.36 (2.69\u0026ndash;10.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComplacency\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplacent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-complacent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.69 (2.81\u0026ndash;7.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.95 (1.33\u0026ndash;6.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConvenience\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Convenient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConvenient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.47 (9.39\u0026ndash;28.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.72 (3.50\u0026ndash;17.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: bold p-values are statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ref\u0026thinsp;=\u0026thinsp;reference group\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. \u003cb\u003ePerceived barriers to COVID-19 vaccination\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eIn an attempt to understand why 14% (82) of the healthcare workers have not been vaccinated, we explored the reasons for non-vaccination among the unvaccinated respondents. We adapted the World Health Organization BeSD framework (WHO, 2022) to assess and address behavioral and social drivers for low uptake. We analyzed the reasons for non-vaccination among the unvaccinated health workers using simple percentages and classified their responses into three domains of the BeSD framework. In Fig.\u0026nbsp;2, the chart reveals that vaccine safety and efficacy (58%) were the top reasons for non-vaccination. These reasons are related to the thinking and feeling behaviors of the respondents. Also, access to vaccines and a busy work schedule (16%) are other reasons for non-vaccination, which express practical barriers to vaccination. And lastly, decision autonomy (6%) proved to be another strong reason for non-vaccination relating to the social process domain.\u003c/p\u003e\u003cp\u003eRespondents in the qualitative interview reported a lack of confidence in the efficacy and safety of the vaccine due to rumors circulating in various news outlets and on social media. Few expressed concerns that the vaccines were manufactured too quickly and lack of confidence in the vaccine manufacturers\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003ethe efficacy, the safety of the vaccine, frankly speaking from all that I have read, from all I understand and ehn as a health worker who understand the process of vaccine production you know to me this vaccine came rather too quick\u0026hellip;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(FGD with Doctor)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e...I am beginning to suspect that even the company that produces this vaccine, maybe the money they have sunk into producing this vaccine, they want to recoup that money, so they keep on promoting the idea of people taking this vaccine whether they like it or not\u0026hellip;\u003c/p\u003e\u003cp\u003e\u003cb\u003e(FGD with Doctor)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study assessed COVID-19 acceptance and hesitancy among healthcare workers in Nigeria. Our attempt was to find out the level of acceptance among the different cadres of health specialties that we have in Nigeria. In the analysis of various job specialties, it was observed that community health workers and doctors exhibited a higher acceptance rate compared to other specialties. It is worth noting that there is a scarcity of studies reporting a higher acceptance rate, specifically among community health workers. Our result could be attributed to several factors. For our study, community health workers make up the highest number of respondents. A justification for this is the way the Nigeria health system is structured, to have more primary health centers than others and community health workers are found here. Also, Community health workers\u0026rsquo; close relationship with communities and the kind of training they receive in disease prevention and control contributes may to the high uptake of the Covid-19 vaccines. Community health workers are present within local communities, serving as the initial point of contact for individuals and playing a crucial role in encouraging healthy practices among community members. As part of their specialized training, they gain access to comprehensive knowledge about diseases and the significance of vaccines. Through this training and their responsibilities, community health workers develop a sense of assurance and confidence in receiving vaccinations. We also found that doctors, aside from community health workers, have a higher acceptance rate than other specialties. This finding is unique given the number of doctors that form the sample from the study. The high acceptance rate of doctors could be explained by the fact that as much as all health workers are at risk of being infected, doctors have a higher risk because of frequent exposure to infected persons and first-hand knowledge or information from patients about the virus. Also, Physicians' access to vaccine information, their education and knowledge of the vaccine development process, and their role in promoting the health of patients may be attributed to their higher acceptance of the Covid-19 vaccine. Our findings mirror other studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported that physicians have a high acceptance rate and higher odds of being tally vaccinated than other specialties in Nigeria. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] study to access health worker vaccine acceptance in Ethiopia. He found that health workers other than doctors and nurses are more likely to be vaccine-hesitant. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] found that medical health workers are pre-dispose to accept the Covid-19 vaccine because of their education and trust in the health system and their understanding of the importance of vaccines and the vaccine manufacturing process.\u003c/p\u003e\u003cp\u003eWe then adopted the multiple logistics regression of the 3C model to assess the probability of COVID-19 acceptance among health workers. From the analysis, we found that the 3C model is a significant predictor of COVID-19 vaccine acceptance among health workers. The analysis revealed that among the 3C predictors of confidence, convenience, and complacency, convivence was a bigger predictor of vaccine acceptance. A plausible reason for convenience is a bigger predictor of vaccine acceptance among health workers in Nigeria can be attributed to the busy schedule of most health workers. Health workers are often overworked. They are always faced with numerous work responsibilities that demand their time and attention, mostly in low-resourced settings. They always expected to work long hours seeing many patients, with additional administrative duties, leaving them with no time to get vaccination, except the facility is a vaccination center. Also, there is little or no flexibility in the work schedule of most health workers in Nigeria, making it challenging for professional health workers to find a convenient time to visit vaccination centers, which work mostly during regular working hours. in their study, Ashok et al. (2021) revealed that if health workers are offered convenience-based incentives such as prices and transportation, they will pre-dispose to accepting the Covid-19 vaccine.\u003c/p\u003e\u003cp\u003eIn our attempt to understand the barriers to COVID-19 vaccination among health workers, we adapted the WHO\u0026rsquo;s Behavioural and Social Drivers (BeSD) of vaccination framework to examine the barriers to COVID-19 vaccination among health workers. Using simple percentages to classify health workers\u0026rsquo; responses according to 3 domains of the BeSD framework (thinking and feeling behavior, practical barriers and social process domain), we found responses around the thinking and feeling domain were the main barriers to COVID-19 vaccination among health workers. These responses relate mostly to issues around vaccine safety and efficacy. Health workers in Nigeria as expected, have access to more information about vaccine production and vaccine adverse effects. As studies such as that of [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] have shown, there are a lot of misinformation and conspiracy theories about the efficacy, effectiveness adverse effects of Covid-19 vaccines available in the country. These information and conspiracy theories are mostly distributed by trusted sources. This information creates skepticism causing health workers to be hesitant about taking the vaccines, especially in communities. There are also concerns about the relatively short period used in developing the Covid-19 vaccine. The urgent need to curb the spread of the COVID-19 pandemic necessitated accelerating the timeline for the production of COVID-19 vaccines compared to traditional vaccine development. This brought concerns about the efficacy, effectiveness, and long-term effect of the COVID-19 vaccine developed, especially among health professionals, leading to hesitancy. In Latvia, [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], found that concerns around vaccine safety and efficacy were the main barriers among health workers. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] found concerns around vaccine safety, efficacy and the potential side effect of the COVID-19 vaccines were the reasons for vaccine hesitancy among health workers.\u003c/p\u003e"},{"header":"5. Strength and Limitation","content":"\u003cp\u003eFor strength, the adoption of the mixed method design for our study offers several advantages, such as improving the quality and comprehensiveness of the research outcome. By triangulating data from both qualitative and quantitative responses, the study is able to establish a more robust and comprehensive understanding of COVID-19 vaccine behaviour of health workers. triangulation of the different data sources enhances the validity of our findings and allows us to situate the findings with the socio-economic, cultural and healthcare settings of Nigeria. Also, The timing of our study gives room for a better insight to design strategies and interventions that will help conduct a mop-up exercise for vaccinated health workers in Nigeria. By conducting this study at this time and the finding from it, we are able to get a first-hand understanding of the specific barriers to vaccination among health workers in Nigeria. With this understanding, a tailored and specific intervention can be designed to reach the unvaccinated population of health workers in Nigeria.\u003c/p\u003e\u003cp\u003eFor the limitations, Although the data for this study was collected from six states to represent the six geographical regions in Nigeria. But the behavioral characteristics of the people from the selected states are most times different from those of the other states in the same region. Hence, the data collected from the participants from the selected states may not represent that of the entire population of health workers due to sampling and self-reporting.\u003c/p\u003e\u003cp\u003eParticipants for this study were self-selected for this, which may have caused some biases based on sample selection, hereby limiting the generalization of the result to all health workers. Also, health workers who volunteered or are self-selected to be part of this study might have vaccines, thereby creating a bias towards other health workers who may have other views about the vaccines. This can cause the data collected to be skewed. Another point to note is that participants who self-select into studies may provide responses that are favorable and acceptable, making results show favorable.\u003c/p\u003e"},{"header":"6. Implication","content":"\u003cp\u003eOur findings revealed a higher acceptance rate of COVID-19 vaccine for CHWs and physicians when compared to their specialties. To bridge the gap between specialties, there is a need for efforts or interventions that encourages peer-to-peer mentoring and coaching or collaborative learning among the different health specialization, where positive experiences and learnings from vaccination can be shared. We also found that convenience was the primary predictor of vaccine behavior among health workers when testing against 3C variables. There is a need for policy and programmatic efforts toward convenient access to vaccination services for health workers in Nigeria. This can be done by establishing vaccination centers where they are easily accessible for all health workers, such as in their health establishments, making the vaccination schedule flexible to allow for vaccination at any time and reducing administrative barriers for health workers that is associated with vaccination, this includes reducing vaccination waiting time, as well as reducing paperwork.\u003c/p\u003e\u003cp\u003eWe adopted the BeSD framework to understand barriers to vaccination among health workers in Nigeria. We found that issues around the thinking and feeling domain have the main barriers to vaccination among health workers. This calls for campaigns that focus on addressing vaccine infodemics and conspiracy theories around vaccines. To do this, there is a need for focused educational campaigns that will provide credible and verified information about the efficacy and safety of vaccines available in Nigeria. Attempts should be made to address the concern of health workers around the development of the vaccines. Finally, efforts should also be made to build the confidence of health workers around the COVID-19 vaccines. These can be achieved by providing fact-based and research-domain information that addresses doubts amongst health workers.\u003c/p\u003e\u003cp\u003eAs at the time of this study, it is mostly public health facilities that are designated COVID-19 vaccination centres in Nigeria. There is a need to address this disparity which makes it difficult for most health workers to get vaccinated. There is need to improve on the distribution channels of the COVID-19 vaccination program in Nigeria. This can be achieved by establishing a partnership that involves public health authorities and the private sector to enable private health establishments to administer the COVID-19 vaccine. This partnership will enable more health facilities offer COVID-19 vaccination scenes, thereby increasing accessibility especially to health workers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eAll necessary research ethical process was met for this study. We obtained ethical approval from John Hopkins University International Review Board (Approval Number: IRB00017765). In Nigeria, ethical approval was obtained from the National Health Research Ethics Committee of Nigeria (NHREC/01/01/2007-27/10/2021). in all the selected states, except Lagos, where ethical approval was weived, we were granted approved by the relevant committees; Kano (NHREC/17/03/2018), Gombe (MOH/ADM/621/Vol.1/385), Kogi (MOH/PRS/465/V.1/018), Enugu (MH/MSD/REC21/244), and Rivers (RSUTH/REC/2021128).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eWe used both written and oral methods to obtain consent from the respondents for the qualitative and quantitative interviews. We used standard oral consent to obtain permission from each respondent. For all interviews, we detailed the study\u0026rsquo;s objective and assured them of the confidentiality of all information provided. We also provided phone credit one thousand naira as incentives to offset data costs for the quantitative interviews conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration:\u0026nbsp;\u003c/strong\u003eThis study was funded by Johnson \u0026amp; Johnson, with funding number 990095573-9100000000-138510. Funding acquisition was by Chizoba Wonodi. The funder had no influence on the analysis or interpretation of the findings presented in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe dataset generated and analyzed during the study are not publicly available due to privacy and ethical restrictions but will be provide upon reasonable request by the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e authors declare no conflict of interest\u003c/p\u003e\u003cp\u003eAuthor Contributions: Conceptualization, C.B.W., P.U.A., I. A.O., A.A.A., and S.N.; Methodology, P.U.A., I.A.O., and A.A.A.; validation, C.B.W., and S.N.; Formal analysis, I. A.O., P.U.A., and A.A.A.; investigation, C.B.W., P.U.A., I. A.O., A.A.A., and S.N.; resources, C.B.W., and S.N.; writing\u0026mdash;original draft preparation P.U.A., I.A.O., and A.A.A.; writing\u0026mdash;review and editing, C.B.W., P.U.A., I. A.O., A.A.A., and S.N.; visualization, P.U.A., I.A.O., and A.A.A.; project administration, C.B.W., and S.N.; funding acquisition C.B.W., and S.N..\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBabatope T, Ilyenkova V, Marais D. COVID-19 vaccine hesitancy: a systematic review of barriers to the uptake of COVID-19 vaccine among adults in Nigeria. Bul Nat Res Cen. 2023;47(1):45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlu-Abiodun O, Abiodun O, Okafor N. COVID-19 vaccination in Nigeria: A rapid review of vaccine acceptance rate and the associated factors. PLoS ONE. 2022, 17(5).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO)\u0026lrm;. 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Conspiracy theories and misinformation about COVID-19 in Nigeria: Implications for vaccine demand generation communications. Vaccine. 2022;40(13):2114\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlatunji OS, Ayandele O, Ashirudeen D, Olaniru OS. Infodemic in a pandemic: COVID-19 conspiracy theories in an African country. Soc Health Beh. 2020;3(4):152.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOyeyemi SO, Fagbemi S, Busari II, Wynn R. Health workers\u0026rsquo; beliefs in COVID-19 conspiracy theories, level of trust in government information and their willingness to take COVID-19 vaccines: A survey from Nigeria (Preprint). JMIR Form Res 2023 7, e41925.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLielsvagere-Endele S, Kolesnikova J, Puzanova E, Timofejeva S, Millere I. Motivators and barriers to COVID-19 vaccination of healthcare workers in Latvia. Fron Psy. 2022, 13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBiswas N, MustMoucheraud C, Phiri K, Whitehead HS, Songo J, Lungu E, Chikuse E, Phiri S, Van Oosterhout JJ, Hoffman RM. Uptake of the COVID-19 vaccine among healthcare workers in Malawi. Int Health. 2022;15(1):77\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7696059/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7696059/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNigeria had enough COVID-19 vaccines to meet its set target, yet vaccine uptake challenges persisted, even among healthcare workers. Part of a broader study conducted in Ethiopia, Kenya, and Nigeria that explored COVID-19 vaccine behavior. This study adopted the 3C and BeSD model to examine COVID-19 vaccine acceptance and hesitancy among healthcare workers in Nigeria. We conducted a mixed-method study involving 654 healthcare workers across Nigeria from February 8 to March 30, 2022. Quantitative data was analyzed using R (version 4.1.1) in R-Studio, The statistical results produced were descriptive, bivariate, and multivariate analyses. The qualitative analysis involved using Dedoose software. 86% of the surveyed healthcare workers are vaccinated. Community health workers and medical doctors boast over 90% vaccination rates compared to other specialties. Multivariate analysis highlights convenience as the strongest driver of COVID-19 vaccination (adjOR\u0026thinsp;=\u0026thinsp;7.72, 95% CI: 3.50\u0026ndash;17.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Barriers to vaccination using the BeSD model include vaccine safety and efficacy concerns, relating to the thinking and feeling domain, followed by access to vaccines and a busy work schedule, which express practical barriers to vaccination. There is need for policy action to combat infodemics and ensure COVID-19 vaccine access for Nigerian healthcare workers.\u003c/p\u003e","manuscriptTitle":"COVID-19 Vaccine Acceptance and Hesitancy Among Healthcare Workers in Nigeria. A Mixed-Method Analysis Using the WHO 3C and BeSD Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 20:25:13","doi":"10.21203/rs.3.rs-7696059/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c72b6986-e467-4ddc-ad56-6673b1f30c93","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T09:28:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 20:25:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7696059","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7696059","identity":"rs-7696059","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}