Determinants of Influenza Vaccination among Healthcare Workers
Bibliographic record
Abstract
OBJECTIVE: To identify the determinants of influenza vaccination and the moderators of the intention-behavior relationship among healthcare workers (HCWs). DESIGN: Prospective survey with 2-month follow-up. SETTING: Three university-affiliated public hospitals. PARTICIPANTS: Random sample of 424 HCWs. METHODS: The intention of an HCW to get vaccinated against influenza was measured by means of a self-administered questionnaire based on an extended version of the theory of planned behavior. An objective measure of behavior was extracted 2 months later from the vaccination database of the hospitals. RESULTS: Controlling for past behavior, we found that the determinants of influenza vaccination were intention (odds ratio [OR], 8.32 [95% confidence interval {CI}, 2.82-24.50]), moral norm (OR, 3.01 [95% CI, 1.17-7.76]), anticipated regret (OR, 2.33 [95% CI, 1.23-4.41]), and work status (ie, full time vs part time; OR, 1.99 [95% CI, 1.92-3.29]). Moral norm also interacted with intention as a significant moderator of the intention-behavior relationship (OR, 0.09 [95% CI, 0.03-0.30]). Again, apart from the influence of past behavior, intention to get vaccinated was predicted by use of the following variables: attitude (beta=.32; P<.001), professional norm (beta=.18; P<.001), moral norm (beta=.18; P<.001), subjective norm (beta=.09; P<.001), and self-efficacy (beta=.08; P<.001). This latter model explained 89% of the variance in HCWs' intentions to get vaccinated against influenza during the next vaccination campaign. CONCLUSIONS: Our study suggests that influenza vaccination among HCWs is mainly a motivational issue. In this regard, it can be suggested to reinforce the idea that getting vaccinated can reduce worry and protect family members.
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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 | 0.000 |
| 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) | 0.002 | 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".