Impact of a decision-aid tool on influenza vaccine coverage among HCW in two French hospitals: A cluster-randomized trial
Bibliographic record
Abstract
INTRODUCTION: Nosocomial outbreaks of seasonal influenza are frequent, and vaccination is largely recommended for healthcare workers (HCWs). Vaccine coverage in French HCWs does not exceed 20%. Decision-aids (DA) are potential useful interventions to increase vaccine coverage (VC). Our aim was to evaluate the impact of a DA on HCWs influenza vaccine coverage. MATERIAL AND METHODS: Prospective cluster-randomized trial conducted in 83 departments in two public hospitals (a teaching and a non-teaching hospital) during the 2018-2019 flu season. Distribution of the DA and of questionnaire about decisional conflict and knowledge in the departments randomized in the intervention group. RESULTS: A total number of 3 547 HCWs were concerned by the study (1 953 in the intervention group, 1 594 in the control group). Global VC was 35.6% during the 2018-2019 season, instead of 23.6% in the 2017-2018 season (p < 0.005). During the 2018-2019 season, VC was 31% (95% CI 28.7-33.3) in the control group and 38.7% (95% CI 36.5-40.9) in the intervention group (p < 0.005). Among the 158 HCWs exposed to the DA who answered the survey, 51.3% had no decisional conflict. HCWs without decisional conflict were more prone to get vaccinated before flu season. CONCLUSION: The use of the DA was associated with a 25% relative increase in VC among HCWs against seasonal influenza. This modest increase remained far from the WHO 75% target, but may have reduced the number of nosocomial. Multi-component interventions are needed to increase VC in HCWs.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".