Cohort profile: Recruitment and retention in a prospective cohort of Canadian health care workers during the Covid-19 pandemic
Bibliographic record
Abstract
Purpose Health care workers (HCWs) were recruited early in 2020 to chart effects on their health as the COVID-19 pandemic evolved. The aim was to identify modifiable workplace risk factors for infection and mental ill-health. Participants Participants were recruited from four Canadian provinces, physicians (MDs) in Alberta, British Columbia, Ontario and Quebec, registered nurses (RNs), licensed practical nurses (LPNs) and health care aides (HCAs) in Alberta and personal support workers (PSWs) in Ontario. Volunteers gave blood for serology testing before and after vaccination. Cases with COVID-19 were matched with up to 4 referents in a nested case-referent study. Findings to Date 4964/5130 (97%) of those recruited joined the longitudinal cohort: 1442 MDs, 3136 RNs, 71 LPNs, 235 PSWs, 80 HCAs. Overall, 3812 (77%) were from Alberta. Pre-pandemic risk factors for mental ill-health and respiratory illness differed markedly by occupation. Participants completed questionnaires at recruitment, fall 2020, spring 2021, and spring 2022. By the 4 th contact, 127 had retired, moved away or died, for a response rate of 89% (4299/4837). 4567/4864 (92%) received at least one vaccine shot: 2752/4567 (60%) gave post-vaccine blood samples. Ease of accessing blood collection sites was a strong determinant of participation. Among 533 cases and 1697 referents recruited to the nested case-referent study, risk of infection at work decreased with widespread vaccination. Future Plans Serology results (concentration of immunoglobulin G (IgG)) together with demographic data will be entered into the publicly accessible database compiled by the Canadian Immunology Task Force. Linkage with provincial administrative health databases will permit case validation, investigation of longer-term sequalae of infection and comparison with community controls. Analysis of the existing dataset will concentrate on effects on IgG of medical condition, medications and stage of pregnancy, and the role of occupational exposures and supports on mental health during the pandemic. Strengths and limitations Recruitment of a broad spectrum of health care workers close to the start of the COVID-19 pandemic through their professional organizations Consent to link to records held by public health departments allows for validation of self-reports of vaccinations and episodes of COVID-19 infection Repeated contacts permit charting the evolution of anxiety, depression and sources of stress through the course of the pandemic The inclusion of a nested case-referent study allows exposure reporting in near real time The absence of good denominator data limits the ability to examine recruitment bias N=4216
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".