Measuring the impact of a mandatory province-wide vaccinate-or-mask policy on healthcare worker absenteeism in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: Vaccinate-or-mask (VOM) policies aim to improve influenza vaccine coverage among healthcare workers (HCW) and reduce influenza-related illness among patients and staff. In 2012, British Columbia (BC) implemented a province-wide VOM influenza prevention policy. This study describes an evaluation of policy impacts on HCW absenteeism rates from before to after policy implementation. METHODS: Using payroll data from regional and provincial Health Authorities (HA), we assessed all-cause sick rates (sick time as a proportion of sick time and productive time) before (2007-2011, excluding 2009-2010) and after (2012-2017) policy implementation, and during influenza season (December 1-March 31) and non-influenza season (April 1-November 30). We used a two-part negative binomial hurdle model to calculate odds ratios (OR) of taking any sick time, relative rates (RR) of sick time taken, and predicted mean sick rates, adjusting for age group, sex, job type, job classification, HA, year and vaccine effectiveness. RESULTS: During influenza season, HCWs in the post-policy period were less likely to take any sick time (OR 0.989, 95%CI: 0.979-0.999) but had higher rates of sick time (RR 1.038, 95%CI: 1.030-1.045). However, during non-influenza season, HCWs in the post-policy period were more likely to take any sick time (OR 1.015, 95%CI: 1.008-1.022) but had lower rates of sick time (RR 0.971, 95%CI: 0.966-0.976). There was an overall increase in predicted mean sick rate from pre to post-policy in influenza season (4.392% to 4.508%) and non-influenza season (3.815% to 3.901%). CONCLUSIONS: The observed year-round increase in sick rates from pre-to-post policy was likely influenced by other factors; however, opposite trends in how HCWs took sick time in the influenza and non-influenza seasons may reflect policy influences and need further research to explore reasons for these differences.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".