Availability, use and impact of workplace mental health supports during the COVID-19 pandemic in a Canadian cohort of healthcare workers
Bibliographic record
Abstract
Abstract The COVID-19 pandemic focused attention on workplace mental health (MH) supports for healthcare workers (HCWs). Methods HCWs in a Canadian cohort reported availability and use of workplace MH supports in October 2020, April 2021 and 2022. At recruitment (April-October 2020) they reported pre-pandemic MH. They completed the Hospital Anxiety and Depression Scale (HADS) at each contact. Availability and use of supports were examined by pandemic phase, workplace, work role and, for use, gender, age, pre-pandemic and current MH. Impact was assessed as MH in 2021/2022 following use in 2020. Results Reports of availability, use and HADS scores were obtained from 4400 HCWs working with patients. Access to MH supports increased during the pandemic, with 94% reporting access to some workplace support by 2022. Half the HCWs had at least one clinically significant HADS score during the pandemic. The proportion with high anxiety scores decreased from 29% to 24% as the pandemic progressed: proportions with high depression scores remained close to 10%. Those with a history of pre-pandemic or current mental ill-health formed the majority of HCWs using MH supports. 25% of those with high HADS scores did not use supports, with depressed males least likely to report use. HCWs using an Employment Assistance Program at the 2 nd contact had lower HADS scores at next follow-up but this was not sustained. Conclusion HCWs reported increasing availability and use of MH supports as the pandemic progressed but one in four of those with anxiety and, particularly, depression did not seek support.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".