Determinants of COVID-19 Vaccine Acceptance in Six Lower- and Middle-Income Countries
Bibliographic record
Abstract
Abstract Background: While a vaccine is the only clinical preventive measure to control the infection and mortality caused by SARS-CoV-2 (COVID-19), delayed acceptance or refusal of COVID-19 vaccines may increase and prolong the threat to global public health and the economy. Identifying behavioural determinants is considered a critical step in explaining and addressing the barriers of vaccine refusal, but there is a lack of evidence around COVID-19 vaccine refusal and delay from a behavioural perspective. This study aims to identify the behavioural determinants of COVID-19 vaccine acceptance and provide recommendations to design actionable interventions to increase the uptake of the COVID-19 vaccine in six lower-and-middle income countries. Methods: Taking into consideration the Health Belief Model (HBM), Theory of Reasoned Action (TRA), and other behavioural models, a Barrier Analysis (BA) approach was employed to examine twelve potential behavioural determinants of vaccine acceptance in Bangladesh, India, Myanmar, Kenya, the Democratic Republic of Congo, and Tanzania. In all six countries, at least 45 interviews with those who intended to take the vaccine (“Acceptors”) and another 45 or more interviews with those who did not (“Non-Acceptors”) were conducted, totalling 542 interviews. Data analysis was performed to find statistically significant (a p-value of less than 0.05) differences between Acceptors and Non-acceptors and to identify which beliefs were most highly associated with acceptance and non-acceptance of the behaviour based on estimated relative risk (ERR). Results: The analysis showed that perceived social norms, perceived positive and negative consequences, perceived risk of getting COVID-19, perceived severity of COVID-19, trust in COVID-19 vaccines, perceived safety of COVID-19 vaccines, and expected access to COVID-19 vaccines had the highest association with COVID-19 vaccine acceptance in Bangladesh, Kenya, Tanzania, and DRC. Additional behavioural determinants found to be significant in both Myanmar and India were perceived self-efficacy, trust in COVID-19 information provided by leaders, perceived divine will, and perceived action efficacy of the COVID-19 vaccines. The study also identified important perceptions and beliefs around COVID-19 and its severity, advantages and disadvantages of being vaccinated, and action efficacy of the vaccine to control the spread of the virus. Conclusion: Many of the determinants found to be significant and their level of significance varied from country to country. National and local plans should include messages and activities that address the behavioural determinants found in this study in order to significantly increase the uptake of COVID-19 vaccine across these countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".