COVID-19 infection and vaccination rates in healthcare workers in British Columbia, Canada: A Longitudinal Urban versus Rural Analysis of the Impact of the Vaccine Mandate
Bibliographic record
Abstract
Abstract Purpose Healthcare workers (HCWs) play a critical role in responding to the COVID-19 pandemic. Early in the pandemic, urban centres were hit hardest globally; rural areas gradually became more impacted. We compared COVID-19 infection and vaccine uptake in HCWs living in urban versus rural locations within, and between, two health authorities in British Columbia (BC), Canada. We also analyzed the impact of a vaccine mandate for HCWs. Methods We tracked laboratory-confirmed SARS-CoV-2 infections, positivity rates, and vaccine uptake in 29,021 HCWs in Interior Health (IH) and 24,634 HCWs in Vancouver Coastal Health (VCH), by occupation, age, and home location, comparing to the general population in that region. We then evaluated the impact of infection rates as well as the mandate on vaccination uptake. Results By October 27, 2021, the date that unvaccinated HCWs were prohibited from providing healthcare, only 1.6% in VCH yet 6.5% in IH remained unvaccinated. Rural workers in both areas had significantly higher unvaccinated rates compared with urban dwellers. Over 1,800 workers, comprising 6.4% of rural HCWs and 3.3% of urban HCWs, remained unvaccinated and set to be terminated from their employment. While the mandate prompted a significant increase in second doses, the impact on the unvaccinated was less clear. Conclusions As rural areas often suffer from under-staffing, loss of HCWs could have serious impacts on healthcare provision as well as on the livelihoods of unvaccinated HCWs. Greater efforts are needed to understand how to better address the drivers of rural-related vaccine hesitancy as the pandemic continues.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".