Physical, Psychological, and Cognitive Profile of Post-COVID Conditions in Healthcare Workers, Quebec, Canada
Bibliographic record
Abstract
Background: The prevalence of post-COVID conditions (PCC) and associated physical, psychological, and cognitive symptoms was assessed among Quebec healthcare workers (HCWs) with coronavirus disease 2019 (COVID-19). Methods: This case-control study compared 6061 symptomatic HCWs with polymerase chain reaction-confirmed COVID-19 between July 2020 and May 2021 with a random sample of 4390 symptomatic HCWs who were test-negative controls. The prevalence of physical symptoms lasting ≥4 weeks (PCC4w) or ≥12 weeks (PCC12w) was estimated among hospitalized and nonhospitalized cases. In multivariate models, sociodemographic and clinical characteristics, as well as vaccine history, were evaluated as potential risk factors. Prevalence ratios compared 4 aspects of self-reported cognitive dysfunction among PCC cases to controls, adjusting for psychological distress and fatigue. Results: PCC4w and PCC12w prevalences of 46% (2746/5943) and 40% (653/1746), respectively, were observed among nonhospitalized cases and 76% (90/118) and 68% (27/37), respectively, among hospitalized cases. Hospitalization, female sex, and age were associated with higher PCC risk. A substantial proportion of nonhospitalized PCC4w cases often or very often reported cognitive dysfunction, including concentration (33%) or organizing (23%) difficulties, forgetfulness (20%), and loss of necessary items (10%). All 4 aspects of cognitive dysfunction were associated with PCC4w symptoms, psychological distress, and fatigue. Conclusions: PCC may be a frequent sequela of ambulatory COVID-19 in working-age adults, with important effects on cognition. With so many HCWs infected, the implications for quality healthcare delivery could be profound if cognitive dysfunction and other severe PCC symptoms persist in a professionally disabling way. Further evaluation of PCC prevalence and prognosis is warranted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".