Filling Data Gaps in Access to Mental Health and Substance Use Services
Bibliographic record
Abstract
Improving access to mental health and substance use (MHSU) services continues to be an area of growing concern in Canada, amplified by the consequences of the COVID-19 pandemic. It was also identified as a priority for federal, provincial and territorial governments in the Shared Health Priorities (SHP) work (CIHI n.d.a.). As part of the SHP work, the Canadian Institute for Health Information recently released 2022 results for two newly developed indicators that help to fill data and information gaps in understanding access to MHSU services in Canada. The first, "Early Intervention for Mental Health and Substance Use among Children and Youth," showed that three in five children and youth (aged 12-24 years) with self-reported early needs accessed at least one community MHSU service in Canada. The second, "Navigation of Mental Health and Substance Use Services," revealed that two out of five Canadians (15 years and older) who accessed at least one MHSU service said that they always or usually had support navigating their services.
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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 | 0.068 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".