Vaccine hesitancy educational interventions for medical students: A systematic narrative review in western countries
Bibliographic record
Abstract
Physician recommendations can reduce vaccine hesitancy (VH) and improve uptake yet are often done poorly and can be improved by early-career training. We examined educational interventions for medical students in Western countries to explore what is being taught, identify effective elements, and review the quality of evidence. A mixed methods systematic narrative review, guided by the JBI framework, assessed the study quality using MERSQI and Cote & Turgeon frameworks. Data were extracted to analyze content and framing, with effectiveness graded using value-based judgment. Among the 33 studies with 30 unique interventions, effective studies used multiple methods grounded in educational theory to teach knowledge, skills, and attitudes. Most interventions reinforced a deficit-based approach (assuming VH stems from misinformation) which can be counterproductive. Effective interventions used hands-on, interactive methods emulating real practice, with short- and long-term follow-ups. Evidence-based approaches like motivational interviewing should frame interventions instead of the deficit model.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".