Future public health emergencies and disasters: sustainability and insights into support programs for healthcare providers
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».