Bibliographic record
Abstract
What Is the Issue? The rate of mental illnesses, such as major depressive disorder and generalized anxiety disorder, has significantly increased among people aged 15 and older living in Canada. In Canada, more than 1 in 3f those with mental illnesses do not receive adequate mental health (MH) services, with notable geographic disparities in the availability and quality of services. Specialized MH services remain particularly scarce in remote and rural areas. Providing MH counselling through virtual platforms has the potential to enhance the accessibility of MH services and reduce the stigma associated with in-person services. A review of the clinical effectiveness and evidence-based guidelines is required to help understand the potential role of virtual MH counselling in clinical practice. What Did We Do? To inform decisions regarding the use of virtual MH counselling for people with depression, anxiety, obsessive-compulsive disorder (OCD) or posttraumatic stress disorder (PTSD), we conducted a rapid review and summarized evidence that compared the clinical effectiveness of MH counselling provided in a virtual setting versus in person. We also sought to identify evidence-based guidelines regarding the use of virtual MH counselling for these populations. We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published since 2019. One reviewer screened articles for inclusion based on predefined criteria, critically appraised the included studies, and narratively summarized the findings. What Did We Find? We found 6 systematic reviews (SRs) relevant to the present review that evaluated the clinical effectiveness of MH counselling provided through virtual versus in-person settings. Most SRs and their included randomized controlled trials (RCTs) reported results on the reduction of depression and anxiety, followed by PTSD and OCD. For depression, PTSD and specific anxiety outcomes (generalized anxiety disorder, social anxiety disorder, and panic disorder), the effectiveness of virtual MH counselling in improving these outcomes was comparable to in-person settings. For OCD, results were inconsistent, suggesting virtual MH counselling can be an alternative treatment where in-person MH counselling is not readily available. We found 5 evidence-based guidelines that provide recommendations on the use of virtual MH counselling for adults with depression, anxiety, and PTSD, based mostly on low-quality evidence or expert opinion. We did not find any evidence-based guidelines or relevant recommendations regarding the use of virtual MH counselling for people of any age with OCD nor children and youth with depression, anxiety, and PTSD. Virtual MH counselling is recommended as a first-line intervention for adults with mild depression and for reducing symptoms of anxiety in older adults. Virtual MH counselling is recommended as second-line adjunctive or alternative intervention for adults with moderate-severe depression, certain anxiety disorders, and PTSD. What Does This Mean? Virtual MH counselling may improve clinical outcomes for people with depression, anxiety, OCD, or PTSD and can be used as a comparable or alternative treatment to in-person MH counselling. Virtual MH counselling may address equity issues regarding access to evidence-based MH services where in-person MH counselling is not readily available. Clinicians and health care decision-makers can use the evidence summarized in this review to inform decisions regarding the implementation of virtual MH counselling for adults with depression, anxiety, OCD, or PTSD.
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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 | 0.010 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".