Excess risk of COVID-19 infection and mental distress in healthcare workers during successive pandemic waves: Analysis of matched cohorts of healthcare workers and community referents in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: To investigate changes in risk of infection and mental distress in healthcare workers (HCWs) relative to the community as the COVID-19 pandemic progressed. METHODS: HCWs in Alberta, Canada, recruited to an interprovincial cohort, were asked consent to link to Alberta's administrative health database (AHDB) and to information on COVID-19 immunization and polymerase chain reaction (PCR) testing. Those consenting were matched to records of up to five community referents (CRs). Physician diagnoses of COVID-19 were identified in the AHDB from the start of the pandemic to 31 March 2022. Physician consultations for mental health (MH) conditions (anxiety, stress/adjustment reaction, depressive) were identified from 1 April 2017 to 31 March 2022. Risks for HCW relative to CR were estimated by fitting wave-specific hazard ratios. RESULTS: Eighty percent (3050/3812) of HCWs consented to be linked to the AHDB; 97% (2959/3050) were matched to 14,546 CRs. HCWs were at greater risk of COVID-19 overall, with first infection defined from either PCR tests (OR=1.96, 95%CI 1.76-2.17) or physician records (OR=1.33, 95%CI 1.21-1.45). They were also at increased risk for each of the three MH diagnoses. In analyses adjusted for confounding, risk of COVID-19 infection was higher than for CRs early in the pandemic and during the fifth (Omicron) wave. The excess risk of stress/adjustment reactions (OR=1.52, 95%CI 1.35-1.71) and depressive conditions (OR=1.39, 95%CI 1.24-1.55) increased with successive waves during the epidemic, peaking in the fourth wave. CONCLUSION: HCWs were at increased risk of both COVID-19 and mental ill-health with the excess risk continuing late in the pandemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".