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Record W4396593107 · doi:10.1093/bjsw/bcae053

Service Utilisation of an Innovative Mental Health Counselling Clinic

2024· article· en· W4396593107 on OpenAlexafffundabout
Lin Fang, Yu Lung, G. Hui, Nelson Pang, Malik Smith, Tamana Azizi

Bibliographic record

VenueThe British Journal of Social Work · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsMental healthMedicineEquity (law)NursingMental health servicePublic healthService (business)Family medicinePsychiatryBusiness

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.058
GPT teacher head0.421
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes3
Has abstractyes

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