Delivering Mental Health Care Virtually During the COVID-19 Pandemic: Qualitative Evaluation of Provider Experiences in a Scaled Context
Bibliographic record
Abstract
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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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".