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Record W4410523549 · doi:10.1136/bmjoq-2025-qshu.236

236 SCOPE-KIDS: feasibility study of a collaborative care model for delivering youth urgent mental health care

2025· article· en· W4410523549 on OpenAlexaff
Janet Song, Lauren Riggin, Ze’ev Lewis, Rachel Pokroy, Shirley Shedletsky, Kittie Pang, Kitty Liu, Rosalie Steinberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsScope (computer science)Mental healthMental health careHealth careMental modelMedicinePsychologyNursingComputer sciencePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Introduction In Ontario, Canada, a significant proportion of youth reported mental health challenges in 2019, with nearly 25% experiencing moderate to severe anxiety or depression and almost 20% seriously considering suicide. The COVID-19 pandemic further exacerbated these issues, leading to a 10–15% increase in mental health service utilization and consequent increases in wait times for child/adolescent mental health services. Service gaps widened, and primary care physicians often had limited knowledge of available resources, while access to allied health supports was restricted for those not affiliated with family health teams.Methods To address these challenges, the SCOPE-KIDS pathway was developed as a joint quality improvement initiative between the Sunnybrook Department of Psychiatry and the North Toronto Ontario Health Team. The initiative aimed to expand mental health service delivery and navigation for children and youth. The development and implementation of SCOPE-KIDS took place over one year and involved multiple external stakeholders, including PCPs, child/adolescent psychiatrists, administrative leaders, QI experts, navigational experts, and a patient/family advisory group. The formation of the initiative was guided by Kotter’s framework in leading change. The SCOPE-KIDS team identified four primary goals: (a) improving time to access SCOPE KIDS service, (b) reducing time to physician-based mental health assessments to less than 4 weeks; (c) providing measurement based care; and (d) providing appropriate follow up matched to patient’s disease severity.Results After 20 months, SCOPE-KIDS handled a total of 115 mental health referrals, all received through a mental health navigator and triaged appropriately. Services provided included direct psychiatric consultations, MD-to-MD (indirect) consultations, social work consultations, and community system/resource navigation. 68 out of 115 referrals involved psychiatric consultations by adolescent psychiatrists, and the vast majority of cases (99 out of 115) were based in the City of Toronto. 55 unique PCPs utilized the service, with an average time from referral to first contact of 3 days. Where requested, 79% of cases consulted with a social worker within 1 week of referral and 77% with a psychiatrist within 1 month. The average age of clients referred was 13.3 years. Around 62% of psychiatric consults included a diagnosed anxiety disorder, 34% had an attention or learning disorder, 24% a mood disorder, and 8% a neurodevelopmental disorder like ASD. Few follow-ups were needed beyond the initial consultation.Conclusions The interventions implemented through the SCOPE-KIDS pathway highlight the importance of multidisciplinary collaboration and the need for accessible mental health resources for youth with limited wait times. The successful implementation of this model in one community suggests that other healthcare settings and teams can replicate it to begin bridging service gaps and meeting the rising mental health needs of youth in their communities.

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 imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.003
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.126
GPT teacher head0.495
Teacher spread0.369 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2025
Admission routes1
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