Determinants of Post-COVID Ill-Health in a Cohort of Canadian Healthcare Workers
Bibliographic record
Abstract
OBJECTIVE: We aimed to estimate prevalence of post-COVID conditions (PCCs) among healthcare workers (HCWs) and to identify predisposing factors. METHODS: A cohort of Canadian HCWs completed four questionnaires during the pandemic. At the final questionnaire, HCWs reported conditions attributed to earlier COVID-19. The proportion developing a PCC was estimated. Risk factors were evaluated by logistic regression. RESULTS: Among 4964 HCWs, 995 had one positive COVID test >90 days before completing the final questionnaire. A total of 266 (27%) developed a PCC. Factors predisposing HCWs to a PCC included depression and increased alcohol consumption reported preinfection, chronic ill-health prepandemic, and a perception that the infection was work-related. PCCs were less likely following vaccination. Most HCWs (98%) returned to work within 30 days, with 8% reporting severe PCC (n = 80). CONCLUSIONS: Predisposing factors reflected poor health preinfection. Most conditions were mild.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".