Recommendations for supporting healthcare workers’ psychological well-being: Lessons learned during the COVID-19 pandemic
Bibliographic record
Abstract
Healthcare workers are at risk of adverse mental health outcomes due to occupational stress. Many organizations introduced initiatives to proactively support staff's psychological well-being in the face of the COVID-19 pandemic. One example is the STEADY wellness program, which was implemented in a large trauma centre in Toronto, Canada. Program implementors engaged teams in peer support sessions, psychoeducation workshops, critical incident stress debriefing, and community-building initiatives. As part of a project designed to illuminate the experiences of STEADY program implementors, this article describes recommendations for future hospital wellness programs. Participants described the importance of having the hospital and its leaders engage in supporting staff's psychological well-being. They recommended ways of doing so (e.g., incorporating conversations about wellness in staff onboarding and routine meetings), along with ways to increase program uptake and sustainability (e.g., using technology to increase accessibility). Results may be useful in future efforts to bolster hospital wellness programming.
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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".