P-217 LESSONS FROM HEALTHCARE WORKER RISK OF COVID-19: A 20-MONTH ANALYSIS OF PROTECTIVE MEASURES FROM VACCINATION AND BEYOND
Bibliographic record
Abstract
Abstract Introduction This study examines the impacts of rigorously implemented infection control, public health and occupational health measures in protecting healthcare workers (HCWs), beyond vaccination. Methods We followed a cohort of 21,242 HCWs in Vancouver, British Columbia, Canada for 20 months from the time the pandemic started until rapid antigen testing ended comprehensiveness of data capture. We used Cox regression and test-negative-design to examine differences in SARS-COV-2 infection rates compared to community counterparts, and within the HCW workforce, assessing the role of occupation, testing accessibility, vaccination rates, and vaccine effectiveness over time. Results Nurses, allied health professionals and medical staff in this jurisdiction had a significantly lower rate of infection compared to their age-group community counterparts, at 47.4, 41.8, and 55.3% reduction respectively. Licensed practical nurses and care aides had the highest risk of infection among HCWs, more than double that of medical staff. However, even considering differences in vaccination rates, no increase in SARS-CoV-2 infection was found compared to community rates, with combined protective measures beyond vaccination associated with a 17.7% reduced SARS-COV-2 rate in this workforce overall. Discussion and conclusion Rigorously implemented occupational health, public health and infection control measures, with excellent communication channels, resulted in a well-protected healthcare workforce with infection rates at or below rates in community counterparts. Greater accessibility of vaccination worldwide is essential; however, as protecting this workforce globally also requires considerable health system strengthening in many jurisdictions, we caution against overly focusing on vaccination to the exclusion of other effective elements for wider protection of HCWs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".