Foundry: Early learnings from the implementation of an integrated youth service network
Bibliographic record
Abstract
AIMS: To provide the first profile of the demographic and service characteristics of young people (aged 12-24 years) who access Foundry, a provincial network of integrated youth health and social service centres in British Columbia, Canada and to share early learnings about implementation and service innovation. METHODS: Using a retrospective chart review, we conducted a census of all young people accessing a Foundry centre in a 'proof of concept' phase. Six centres were assessed between October 2015 and March 2018. Data included demographics, mental health service access history, service type the youth was seeking, and information about how they found out about the centre. RESULTS: A total of 4783 young people presented during this proof of concept period, for a total number of 35 791 visits. The most frequently accessed category of service was mental health/substance use (57%) followed by physical health (25%). Young people were most likely to be female, aged 15-19, and White. Youth demographic characteristics showed an over-representation of Indigenous and LGBTQ2 youth and under-representation of males and youth aged 20-24. Youth were most likely to learn about Foundry from a friend (44%) or family member (22%). Most youth (58%) reported that they would have gone 'nowhere' if not for Foundry. CONCLUSIONS: Foundry is a model of integrated health and social services delivery, focused on early intervention, prevention and accessibility, driven by the needs and priorities of young people and their families. Leveraging international integrated youth health service evidence, the model addresses urgent priorities in Canadian health service delivery.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".