Physicians’ Intentions to Recommend Influenza Vaccine: A Multi-Centered Hospital-Based Study Using the Theory of Planned Behavior in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Influenza remains a significant public health challenge in low- and middle-income countries (LMICs) like Bangladesh, where vaccine uptake remains low despite the substantial disease burden. Physicians play a vital role in promoting vaccination, yet their intentions and influencing factors are not well understood. METHODS: We conducted a cross-sectional study from June to October 2022 across four tertiary-level hospitals in Bangladesh using a questionnaire grounded in the Theory of Planned Behavior (TPB). Hierarchical logistic regression was employed to identify factors associated with vaccine recommendation intentions. RESULTS: Among 972 physicians with an average age of 32.1 years, 40.1% intended to recommend and administer the influenza vaccine. Most (85.3%) agreed vaccination reduces risk, 65.5% desired vaccination for self-protection, 63.5% would vaccinate if available at work, and 85.3% anticipated Ministry of Health support. Male (OR = 1.9, 95% CI: 1.5-2.3) and married (OR = 1.5, 95% CI: 1.1-1.9) physicians were more likely to recommend vaccination. Each unit increase in attitude score doubled the likelihood of recommending the vaccine (OR = 2.0, 95% CI: 1.4-3.0). CONCLUSIONS: Physicians' influenza vaccine recommendations in Bangladesh are suboptimal, influenced by gender, marital status, and attitudes. Targeted educational interventions addressing attitudinal barriers and leveraging institutional support could improve recommendation practices.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".