Service Utilisation of an Innovative Mental Health Counselling Clinic
Bibliographic record
Abstract
Abstract Responding to the increasing challenges to mental health equity, Talk It Out Counseling Clinic (the Clinic), an innovative, public-facing counselling service, was established at the University of Toronto during the pandemic. Operating on the principles of anti-oppression and trauma-informed care, the Clinic trains Master of Social Work students to deliver mental health counselling to populations encountering multiple barriers to mental health equity through phone or video. Guided by a quality improvement framework, this study used chart reviews to examine the demographic characteristics, initial mental health status and service utilisation of clients (N = 116) who completed services at the Clinic during its inaugural year. Three-quarters of the clients identified as Black or other racialised individuals, and over 70 per cent rated their general mental health as poor or fair. Nearly 80 per cent of clients referred to the Clinic proceeded to an intake, amongst whom 69.6 per cent successfully completed the services. Warm handoff was associated with service engagement, whilist men and older clients were more likely to terminate the services prematurely. The study underscores the mental health needs of communities confronting multiple challenges and illuminates the processes conducive to client engagement and service delivery within an innovative, school-run mental health clinic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".