Mental health and social program usage: analyses for integrated mental health hubs
Bibliographic record
Abstract
In 2019, Alberta is creating integrated mental health hubs to support the well-being of youth. This report describes the proportion of youth (11 to 24 years old) between 2005/06 and 2010/11 who received mental health diagnostic codes in Alberta and their experiences with provincial services. This report found that (1) the proportion of youth who received diagnostic codes for a mental health condition was 20% overall, but highest (30%) in females 19-24 years old, (2) youth who received a mental health diagnostic code were more likely to have received services from a social program, be involved in the criminal justice system, and have indicators of substance abuse and self-harm behaviours, and (3) between 30% and 45% of older female youth with mental health diagnostic codes experienced pregnancy at least once during the report period. In addition, the report profiled specific service use information about three target community sites for the implementation of integrated mental health hubs. These findings provide policy-relevant evidence that public authorities may consider as they seek to better support children with mental health conditions and create integrated mental health hubs.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".