Providers’ perspectives on implementing resilience coaching for healthcare workers during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic severely exacerbated workplace stress for healthcare workers (HCWs) worldwide. The pandemic also magnified the need for mechanisms to support the psychological wellbeing of HCWs. This study is a qualitative inquiry into the implementation of a HCW support program called Resilience Coaching at a general hospital. Resilience Coaching was delivered by an interdisciplinary team, including: psychiatrists, mental health nurses allied health and a senior bioethicist. The study focuses specifically on the experiences of those who provided the intervention. METHODS: Resilience Coaching was implemented at, an academic hospital in Toronto, Canada in April 2020 and is ongoing. As part of a larger qualitative evaluation, 13 Resilience Coaches were interviewed about their experiences providing psychosocial support to colleagues. Interviews were recorded, transcribed, and analyzed for themes by the research team. Interviews were conducted between February and June 2021. RESULTS: Coaches were motivated by opportunities to support colleagues and contribute to the overall health system response to COVID-19. Challenges included finding time within busy work schedules, balancing role tensions and working while experiencing burnout. CONCLUSIONS: Hospital-based mental health professionals are well-positioned to support colleagues' wellness during acute crises and can find this work meaningful, but note important challenges to the role. Paired-coaches and peer support among the coaching group may mitigate some of these challenges. Perspectives from those providing support to HCWs are an important consideration in developing support programs that leverage internal teams.
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".