Measuring the impact of influenza vaccination on healthcare worker absenteeism in the context of a province-wide mandatory vaccinate-or-mask policy
Bibliographic record
Abstract
OBJECTIVES: In 2012, British Columbia (BC) implemented a province-wide vaccinate-or-mask influenza prevention policy for healthcare workers (HCWs) with the aim of improving HCW coverage, and reducing illness in patients and staff. We assess post-policy impacts of HCW vaccination status on their absenteeism. METHODS: We matched individual HCW payroll data from December 1, 2012 to March 31, 2017 with annually self-reported vaccination status for BC health authority employees to assess sick rates (sick time as a proportion of sick time and productive time). We modelled adjusted odds ratios (OR) of taking any sick time, relative rates (RR) of sick time taken, and predicted mean sick rates by vaccination status in influenza (December 1-March 31) and non-influenza seasons (April 1 to November 30). We used two methods to assess changes in influenza season sick rates for HCWs who had a change in their vaccination status over the five years. RESULTS: HCWs who reported 'early' vaccination (before December 1 when the policy is in effect) were less likely to take sick time (OR 0.874, 95%CI: 0.866-0.881) and took less sick time (RR 0.907, 95%CI: 0.901-0.912) in influenza season compared to HCWs who did not report vaccination; whereas HCWs who reported 'late' (between December 1 and March 31, and subject to masking until vaccinated) had similar sick rates to HCWs who did not report vaccination. These trends were also observed in non-influenza season. Influenza season sick rates were similar for HCWs that had at least one year of 'early' vaccination and one year where vaccination was not reported over the five year period. CONCLUSIONS: Overall absenteeism is lower among HCWs who report vaccination versus those who do not report. However, absenteeism behaviours appear to be influenced by individual level factors other than vaccination status.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".