Leveraging integrated youth services for social prescribing: a case study of Youth Wellness Hubs Ontario
Bibliographic record
Abstract
INTRODUCTION: Integrated youth services (IYS) presents a unique opportunity to adopt social prescribing (SP) strategies within the IYS service model by developing and leveraging a highly connected multidisciplinary network of clinical and community-based service providers to tackle health inequities and enhance service access and outcomes for youth. This paper outlines a case study of Youth Wellness Hubs Ontario (YWHO), Canada, a collective of youth-serving organizations integrated and networked, and operating as a learning health system implementing SP services. The main study objective was to document how YWHO hubs engage in social prescribing through service provision. METHODS: We adopted an embedded case study approach. Data were collected from youth (n = 6361) aged between 12 and 25 years who were seeking services at a YWHO hub. Descriptive analyses, including frequencies across categories, were generated from service data, including reason for visit, needs addressed and service provided. RESULTS: A comparative analysis of services requested and provided found that youth across visits to YWHO hubs were engaging with multiple services and service providers, with a wide range of health, mental health and social support needs being addressed. CONCLUSION: YWHO implements SP services that aim to improve mental health resilience by supporting the vocational, educational and socialization needs of young people accessing IYS through YWHO hubs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".