O-168 COVID-19 infection and mental wellness in a Canadian cohort study of healthcare workers
Bibliographic record
Abstract
Introduction Healthcare workers (HCW) working through the pandemic are in the front line for infection, psychological pressure and overwork. Objectives To identify modifiable work factors associated with COVID-19 infection and mental distress, and to assess the effectiveness of provisions to mitigate their impact. Methods A cohort study of HCWs was set up in the first weeks of the pandemic in Canada. HCWs from British Columbia, Alberta, Ontario, and Quebec completed an online questionnaire in the spring/summer of 2020, and a Phase 2 questionnaire from October 2020. They also provided a blood sample to assess SARS-CoV-2 antibodies. HCWs reporting a COVID-19 infection after the Phase 2 questionnaire were matched on job-type and province to 4 referents for a nested case-referent (C-R) study concentrating on exposures immediately prior to infection. Phase 3 is underway, with a final contact planned for March 2022. Results 5135 HCWs completed the Phase 1 questionnaire with 93% (4539/4857) of those eligible completing Phase 2. By March 1st 2021, 157 cases had been confirmed by PCR and a further 10 found positive only on antibody testing (an overall rate of 3.3%). The odds of infection doubled for working one-on-one with known COVID-19 patients. Rates were lower in physicians and nurses, compared to personal support workers, health care aides, and licensed practical nurses. HCWs in a hospital setting had lower rates than those working in the community, where shortages of personal protective equipment were more widespread. High rates of anxiety (on the Hospital Anxiety and Depression Scale) were recorded in both Phase 1 and 2. Only 1 in 4 HCW had used available mental health supports. By May 2021, 100 cases with 389 referents had been recruited to the on-going C-R study. Conclusion Information collected prospectively has the potential to improve HCWs protection during this and future epidemics.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".