Intersectoral policy for severe and persistent mental illness: review of approaches in a sample of high-income countries
Bibliographic record
Abstract
BACKGROUND: It is increasingly recognised that intersectoral linkages between mental health and other health and support sectors are essential for providing effective care for individuals with severe and persistent mental illness. The extent to which intersectoral collaboration and approaches to achieve it are detailed in mental health policy has not yet been systematically examined. METHODS: Thirty-eight mental health policy documents from 22 jurisdictions in Australia, New Zealand, the United Kingdom, Ireland and Canada were identified via a web search. Information was extracted and synthesised on: the extent to which intersectoral collaboration was an objective or guiding principle of policy; the sectors acknowledged as targets for collaboration; and the characteristics of detailed intersectoral collaboration efforts. RESULTS: Recurring themes in objectives/guiding principles included a whole of government approach, coordination and integration of services, and increased social and economic participation. All jurisdictions acknowledged the importance of intersectoral collaboration, particularly with employment, education, housing, community, criminal justice, drug and alcohol, physical health, Indigenous, disability, emergency and aged care services. However, the level of detail provided varied widely. Where detailed strategies were described, the most common linkage mechanisms were joint service planning through intersectoral coordinating committees or liaison workers, interagency agreements, staff training and joint service provision. CONCLUSIONS: Sectors and mechanisms identified for collaboration were largely consistent across jurisdictions. Little information was provided about strategies for accountability, resourcing, monitoring and evaluation of intersectoral collaboration initiatives, highlighting an area for further improvement. Examples of collaboration detailed in the policies provide a useful resource for other countries.
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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 | 0.029 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.037 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".