Factors Associated with Healthcare Worker Acceptance of Vaccination: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Healthcare workers experience occupational risk of infection and may transmit infections to patients. Vaccination provides an efficient means of protecting workers and patients, but uptake may be low. We sought to identify factors influencing vaccine acceptance by healthcare workers in order to obtain insights leading to more effective vaccination programs in this population. DESIGN: Systematic review and meta-analysis. METHODS: We searched Medline, Embase, and CINAHL databases to identify studies published up to May 2012. Factors influencing vaccination acceptance were devised a priori. Random-effects meta-analysis was performed to generate summary estimates of effect. Heterogeneity and publication bias were explored using statistical tools. RESULTS: Thirty-seven studies evaluating a variety of vaccines (against influenza, pertussis, smallpox, anthrax, and hepatitis B) were included. Homogeneous effects on vaccine acceptance were identified with desire for self-protection (odds ratio [OR], 3.42 [95% confidence interval (CI), 2.42-4.82]) and desire to protect family and friends (OR, 3.28 [95% CI, 1.10-9.75]). Concern that vaccine transmits the illness it was meant to prevent decreased acceptance (OR, 0.42 [95% CI, 0.30-0.58]). Differences in physician and nurse acceptance of immunization were seen between Asian and non-Asian studies. CONCLUSIONS: Consideration of self-protection (rather than absolute disease risk or protection of patients) appears the strongest and most consistent driver of healthcare workers' decisions to accept vaccination, though other factors may also be impactful, and reasons for between-study divergence in effects is an important area for future research. This finding has important implications for the design of programs to enhance healthcare worker vaccine uptake.
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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 | 0.019 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".