Determinants of COVID-19 vaccine acceptance amongst doctors practising in Cross River State, Nigeria
Bibliographic record
Abstract
Background: COVID-19 vaccine is one of the most effective public health intervention approaches for prevention of COVID-19. Despite its well-known efficacy and safety, significant proportion of frontline COVID-19 healthcare workers remain hesitant about accepting the vaccine for whatever reasons. This study aimed to determine acceptance rate and determinants of vaccine refusal among doctors in Cross River State, Nigeria. Methodology: This was a cross-sectional survey of doctors using structured online questionnaire administered via the WhatsApp platform of the medical doctors’ association, in order to assess their rate of acceptance of COVID-19 vaccines, and reasons for vaccine refusal. The predictors of vaccine acceptance were analysed by univariate and multivariate logistic regression analyses. Results: Of the 443 medical doctors targeted on the WhatsApp platform, 164 responded to the questionnaire survey, giving a response rate of 37.0% (164/443). The mean age of the respondents is 38 ±6.28 years, 91 (55.5%) are 38 years old and above, 97 (59.1%) are males and 67 (40.9%) are females, giving a male-to-female ratio of 1.4:1. The greater proportion of the respondents are physicians (70/148, 47.3%) and about three-quarter of the participants (127/164, 77.4%) had received COVID-19 vaccine. The proportion of physicians who had received COVID-19 vaccine (57/70, 81.4%) was more than the proportion of general practitioners (31/42, 73.8%) and surgeons (24/35, 68.6%). Low perceived benefit of vaccination was the main reason given for COVID-19 vaccine refusal (45.9%, 17/37). No significant association was found between vaccine refusal and suspected predictors (p>0.05). Conclusion: Our study revealed high rate of COVID-19 vaccine acceptance among medical doctors especially among the physicians, with the surgeons showing lowest acceptance rate. A significant proportion would not take vaccine because they perceived it lacks much benefits. To raise vaccine acceptance among doctors, more efforts on vaccine literacy that would target doctors from all sub-specialties especially surgeons and incorporate vaccine benefits should be made.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".