Seasonal influenza vaccination campaigns for health care personnel: systematic review
Bibliographic record
Abstract
BACKGROUND: In Canada, vaccination coverage for seasonal influenza among health care personnel remains below 50%. The objective of this review was to determine which seasonal influenza vaccination campaign or campaign components in health care settings were significantly associated with increases in influenza vaccination among staff. METHODS: We identified articles in eight electronic databases and included randomized controlled trials, controlled before-and-after studies and studies with interrupted time series designs in our review. Two reviewers independently abstracted the data and assessed the risk of biases. We calculated risk ratios and 95% confidence intervals for randomized controlled trials and controlled before-and-after studies and described interrupted time series studies. RESULTS: We identified 99 studies evaluating influenza vaccination campaigns for health care workers, but only 12 of the studies were eligible for review. In nonhospital health care settings, including long-term care facilities, campaigns with a greater variety of components (including education or promotion, better access to vaccines, legislation or regulation and/or role models) were associated with higher risk ratios (i.e, favouring the intervention group). Within hospital settings, the results reported for various types of campaigns were mixed. Many of the criteria for assessing risk of bias were not reported. INTERPRETATION: Campaigns involving only education or promotion resulted in minimal changes in vaccination rates. Further studies are needed to determine the appropriate components and combinations of components in influenza vaccination campaigns for health care personnel.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".