Future public health emergencies and disasters: sustainability and insights into support programs for healthcare providers
Bibliographic record
Abstract
BACKGROUND: The mental health of healthcare workers (HCWs) has been at the forefront throughout the COVID-19 pandemic. While workplace-based support programs have been developed in hospitals globally, few systematically collected data. While critical to their success, information on these programs and the experience of mental healthcare providers (MHP) who support colleagues is limited. The objective of this study was to explore the experiences of MHP caring for HCW colleagues within a novel workplace-based mental health support program during the COVID-19 pandemic, to provide insights on facilitators, areas for improvement and barriers to program sustainability. METHODS: This qualitative study used semi-structured interviews conducted by videoconference between September 2020 to October 2021. UHN CARES (University Health Network Coping and Resilience for Employees and Staff) Program was developed during the first wave of the COVID-19 pandemic in March 2020. It supports over 21,000 staff members within the UHN, Canada's largest academic health research institution, in Toronto, Canada. Purposive sampling was used to select 10 of the 22 MHP in the UHN CARES Program (n = 10). Using a critical realism framework, key components required to sustain a successful workplace-based mental health support program for HCWs and balance the needs of MHP were determined. RESULTS: Six psychiatrists and four psychologists (n = 10) with varying roles at UHN participated in 17 interviews, including seven repeat interviews exploring changes over time within the pandemic and program. Components which facilitated the success of the program included flexibility in scheduling, confidential health record storage, comprehensive administrative support, availability of resources and adaptive quality improvement approach. Recommendations for improvement included opportunities for peer supervision, triaging of cases, and managing HCW expectations. MHP found caring for HCWs to be meaningful and they utilized existing clinical skills during sessions. Challenges included working in a virtual setting, navigating boundaries when caring for colleagues, and managing the range of service users and their needs. CONCLUSIONS: These findings suggest how support programs can be structured for HCWs, how to provide support, and how to sustain this support, allowing health systems to balance the needs of HCWs and MHPs in preparation for future public health emergencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".