Effective Modalities of Virtual Care to Deliver Mental Health and Addictions Services in Canada
Bibliographic record
Abstract
The delivery of virtual mental health care by regulated healthcare professionals has grown substantially since the onset of the COVID-19 pandemic. In the limited research conducted on this modality, virtual mental health care has been found to be efficacious for supporting patients with depression, anxiety, and post-traumatic stress disorder. However, there is limited comparative evidence between in-person and virtual modalities, or for severe mental illnesses such as schizophrenia or bipolar disorder. Thus, despite the surge in the use of virtual care during the pandemic, it is important to recognize that virtual care may not be an adequate substitute for in-person treatment for all populations or conditions. Further, while virtual mental health care has the potential to address barriers to access to care for rural and underserved communities, it may also propagate existing inequities in mental health care for under-resourced populations. Many challenges to the delivery of equitable care through virtual mental health remain. Enhancing technological literacy and access for clinicians and clients, and delivering culturally competent care that aligns with the needs of the local population and community is a largely unaddressed priority for advancing transparency, trust and equity. Deliberate consideration of the specific needs and issues, preferences, culture and values of individual patients and communities is important to deliver culturally-competent virtual mental health models of care for equitable, accessible recovery. This should be done through close engagement and collaborative co-creation with patients, mental health researchers, practitioners and communities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".