P-251 HOW DID CANADIAN HEALTHCARE WORKERS DESCRIBE STRESSORS DURING THE COVID-19 PANDEMIC?
Bibliographic record
Abstract
Abstract Introduction Few studies have reported stress factors in healthcare workers (HCW) using open-ended questions, which collect a higher diversity of respondent perceptions than anticipated by researchers. Our objective was to describe stress factors reported by HCW in open-ended questions during the four phases of a large cohort study in Canada. Methods A prospective cohort of 4964 HCW was assembled with physicians, nurses, healthcare aides and personal support workers recruited from Alberta, British Columbia, Ontario and Quebec. Participants completed 4 online questionnaires (phase 1 in spring/summer 2020, phase 2 in fall 2020, phase 3 in spring 2021, phase 4 in spring 2022). Each questionnaire included an open-ended question on stressful events since the start of the pandemic or since the previous questionnaire. Responses were classified into 29 categories. Results Eight stress categories were reported 1000 times or more among the 17,436 questionnaires from the 4 phases. Five categories showed a downward trend over time: fear of COVID-19, difficult access to personal protective equipment, changing guidelines, management of difficult cases, changes to work routine. An increasing trend was noted for volume of work, and poor behavior from the public or staff. Difficulties managing patients’ deaths remained quite steady. Discussion Reporting of most stressors has decreased over the pandemic. However, the volume of work and the poor behavior of the public, patients and coworkers were seen to increase, consistent with reports of overtime work late in the pandemic. Conclusion The expression of stressors in open-ended questions helps identify levers for reducing them.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".