Supporting Healthcare Worker Mental Health during the COVID-19 Pandemic: Learning from Peer Support Facilitators
Bibliographic record
Abstract
Healthcare Workers are at risk of long-term negative mental health effects resulting from the COVID-19 pandemic. The Social Support, Tracking Distress, Education and Discussion Community Staff Wellness Program was implemented in select hospital units during the pandemic. Programming was facilitated by multidisciplinary peer supporters; this project aimed to elucidate their experiences, and gain insight into lessons learned and future directions for the implementation of peer support programming. Two open-ended, semi-structured focus groups were conducted with peer supporters. Transcripts were inductively and iteratively read and coded. Codes were deductively categorized to summarize relevant information. Peer supporters described benefits and challenges of the role, reporting an overall positive experience. Building trust with the target group was highlighted as important to program uptake. When planning similar work, groups should aim to build organizational, as well as individual, resilience by targeting leadership and incorporating non-pathologizing messages regarding wellness in onboarding and routine team meetings.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".