Acceptance of COVID-19 Vaccine and Associated Knowledge, Attitude, Practice (KAP) and Socio-demographic Factors among Resident Doctors
Bibliographic record
Abstract
Background: Despite the global emphasis on the prevention of COVID-19 and other communicable diseases through vaccination, there are still reports of vaccine hesitancy even among healthcare workers. This study provides useful insights on the underlying causes of vaccine hesitancy to guide the development of strategies likely to reduce vaccine hesitancy and strengthen the control of vaccine-preventable infections in a developing country like Nigeria. To evaluate the acceptance of COVID-19 vaccine and the associated KAP and socio-demographic factors among resident doctors at The University of Port Harcourt Teaching Hospital (UPTH) in order to provide information necessary for vaccine enlightenment programmes and for policymakers focused on controlling vaccine-preventable pandemics. Method: A cross-sectional survey of resident doctors at the UPTH was done. A validated self-administered online questionnaire was used to collect the data on the acceptance and KAP of COVID-19 vaccine. Multinomial logistic regression was used to assess the strength of association of socio-demographic variables and KAP with the acceptance of COVID-19 vaccine. Result: The study found a high acceptance rate of 79.7% for the COVID-19 vaccine. Notably, there was a significant link between accepting the vaccine and having a positive attitude towards it (p = 0.0001) and also engaging in good practices (p = 0.001). However, there wasn't a clear connection between vaccine acceptance and having a good knowledge about it (p = 0.606). After adjusting for confounding variables, young adults aged 25 – 30 years showed the strongest relationship to vaccine acceptance when compared to older age groups (AOR= 8.74). Conclusion: The acceptance of COVID-19 vaccine among resident doctors (79.7%) was significantly associated with younger age, good attitude, and good practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".