Developing standards for virtual delivery of mental health services in Canadian primary care: A qualitative study and modified Delphi process
Bibliographic record
Abstract
In Canada, public health measures necessitated by the COVID-19 pandemic resulted in a rapid onset and prolonged, widespread increase in the use of virtual primary care services, including for mental health conditions. Our aim was to develop standards on virtual delivery of mental health services in primary care in Canada using information obtained from an earlier rapid review as well as participant feedback obtained through interviews and a focus group. We developed standards using three interlinked processes. First, we completed a rapid review of guidelines regarding virtual primary mental health care services. We then invited health care workers and people with lived experience of mental health concerns to participate in a focus group and interviews. Finally, members of the study team drafted standards and shared them with an advisory group, who reviewed their feasibility, phrasing, and acceptability through a modified Delphi process. Standards ranked as having less than 100% feasibility and acceptability were brought to a virtual discussion of the advisory group to finalize the list. Seven participants were recruited into the focus group and interviews. We identified three themes: (i) patients' and providers' agreement about expectations regarding virtual care, (ii) accessibility and equity, and (iii) safety planning in the delivery of virtual care. We drafted 18 standards on virtual primary mental health care delivery that were reviewed by an advisory group of identified experts. Thirteen standards were included in the final list. The standards bring attention to continuity of care, and resources and information that should be given to patients to further health equity. These standards provide guidance for the organization and delivery of virtual mental health services in Canadian and international primary care, particularly within the context of single payer health systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".