A mixed-methods study of risk factors and experiences of healthcare workers tested for the novel coronavirus in Canada
Bibliographic record
Abstract
Objectives We aimed to investigate the contribution of occupational and non-work-related factors to the risk of novel coronavirus (SARS-CoV-2) infection among healthcare workers (HCWs) in Vancouver Coastal Health, British Columbia, Canada. We also aimed to examine how HCWs described their experiences. Methods We conducted a matched case-control study using data from online and phone questionnaires with optional open-ended questions completed by HCWs who sought SARS-CoV-2 testing between March 2020 and March 2021. Conditional logistic regression and thematic analysis were utilized. Results Data from 1340 HCWs were included. Free-text responses were provided by 257 respondents. Adjusting for age, gender, race, occupation, and number of weeks since pandemic was declared, community exposure to a known COVID-19 case (adjusted odds ratio -aOR: 2.45; 95% CI 1.67-3.59), and difficulty accessing personal protective equipment -PPE- (aOR: 1.84; 95% CI 1.07-3.17) were associated with higher infection odds. Care-aides/licensed practical nurses had substantially higher risk (aOR: 2.92; 95% CI 1.49-5.70) than medical staff who had the lowest risk. Direct COVID-19 patient care was not associated with elevated risk. HCWs’ experiences reflected the phase of the pandemic when they were tested. Suboptimal communication, mental stress, and situations perceived as unsafe were common sources of dissatisfaction. Conclusions Community exposures and occupation were important determinants of infection among HCWs in our study. The availability of PPE and clear communication enhanced a sense of safety. Varying levels of risk between occupational groups call for wider targeting of infection prevention measures. Strategies for mitigating community exposure and supporting HCW resilience are required.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".