Crisis and Post-Crisis Virtual Mental Health Care: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: Crisis services are often a first point of contact for individuals needing mental health assessment and intervention. The rapid expansion of virtual care in recent years has enabled remote assessment and introduced novel ways to support crisis stabilization in the community. This scoping review aims to summarize the extent of the literature on virtual crisis assessment and intervention models. METHODS: PubMed, PsycINFO, CINAHL, and ProQuest databases were searched for English- and French-language literature published between January 1, 2018, and June 30, 2024. Database search results were imported into the online Covidence review management program. A minimum of two reviewers screened titles and abstracts. Target information was extracted from included full texts and summarized thematically across study characteristics and outcomes. RESULTS: A total of 5,345 titles were reviewed, with 45 publications included. Publications represented models from around the globe supporting youth and/or adult service users. Data synthesis highlighted the feasibility and potential for virtual care models supporting comprehensive crisis assessment (services that go beyond hotline de-escalation and triage), inpatient admission alternatives, and post-crisis follow-up. CONCLUSION: The available literature suggests that virtual crisis care options are growing, especially during and in the aftermath of the COVID-19 pandemic. Although few rigorous evaluations exist, there is strong evidence of feasibility with emerging and encouraging evidence for effectiveness. Further research focused on outcomes, comparisons of virtual and in-person models, and cost-effectiveness is warranted. Additional research could focus on virtual care models for the geriatric population, which is underrepresented in the available literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".