Delivering Mental Health Care Virtually During the COVID-19 Pandemic: Qualitative Evaluation of Provider Experiences in a Scaled Context
Notice bibliographique
Résumé
BACKGROUND: Virtual care delivery within mental health has increased rapidly during the COVID-19 pandemic. Understanding facilitators and challenges to adoption and perceptions of the quality of virtual care when delivered at scale can inform service planning postpandemic. OBJECTIVE: We sought to understand consistent facilitators and persistent challenges to adoption of virtual care and perceived impact on quality of care in an initial pilot phase prior to the pandemic and then during scaled use during the pandemic in the mental health department of an ambulatory care hospital. METHODS: This study took place at Women's College Hospital, an academic ambulatory hospital located in Toronto, Canada. We utilized a multimethods approach to collect quantitative data through aggregate utilization data of phone, video, and in-person visits prior to and during COVID-19 lockdown measures and through a provider experience survey administered to mental health providers (n=30). Qualitative data were collected through open-ended questions on provider experience surveys, focus groups (n=4) with mental health providers, and interviews with clinical administrative and implementation hospital staff (n=3). RESULTS: Utilization data demonstrated slower uptake of video visits at launch and prior to COVID-19 lockdown measures in Ontario (pre-March 2020) and subsequent increased uptake of phone and video visits during COVID-19 lockdown measures (post-March 2020). Mental health providers and clinic staff highlighted barriers and facilitators to adoption of virtual care at the operational, behavioral, cultural, and system/policy levels such as required changes in workflows and scheduling, increased provider effort, provider and staff acceptance, and billing codes for physician providers. Much of the described provider experiences focused on perceived impact on quality of mental health care delivery, including perceptions on providing appropriate and patient-centered care, virtual care effectiveness, and equitable access to care for patients. CONCLUSIONS: Continued efforts to enhance suggested facilitators, reduce persistent challenges, and address provider concerns about care quality based on these findings can enable a hybrid model of patient-centered and appropriate care to emerge in the future, with options for in-person, video, and phone visits being used to meet patient and clinical needs as required.
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Comment cette classification a été obtenuedéplier
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 | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 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 ».