Guidelines and recommendations about virtual mental health services from high-income countries: a rapid review
Bibliographic record
Abstract
OBJECTIVES: This study reviewed existing recommendations for virtual mental healthcare services through the quadruple aim framework to create a set of recommendations on virtual healthcare delivery to guide the development of Canadian policies on virtual mental health services. DESIGN: We conducted a systematic rapid review with qualitative content analysis of data from included manuscripts. The quadruple aim framework, consisting of improving patient experience and provider satisfaction, reducing costs and enhancing population health, was used to analyse and organise findings. METHODS: Searches were conducted using seven databases from 1 January 2010 to 22 July 2022. We used qualitative content analysis to generate themes. RESULTS: The search yielded 40 articles. Most articles (85%) discussed enhancing patient experiences, 55% addressed provider experiences and population health, and 25% focused on cost reduction. Identified themes included: screen patients for appropriateness of virtual care; obtain emergency contact details; communicate transparently with patients; improve marginalised patients' access to care; support health equity for all patients; determine the cost-effectiveness of virtual care; inform patients of insurance coverage for virtual care services; increase provider training for virtual care and set professional boundaries between providers and patients. CONCLUSIONS: This rapid review identified important considerations that can be used to advance virtual care policy to support people living with mental health conditions in a high-income country.
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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 | 0.044 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".