National Mental Health Performance Framework: Descriptive analysis of state and national data for 2019–2020
Bibliographic record
Abstract
OBJECTIVE: To compare key performance indicators for public state and territory specialist mental health services in Australia. METHODS: A descriptive analysis of the publicly-available National Mental Health Performance Framework key performance indicators (KPI), hosted by the Australian Institute of Health and Welfare for 2019-2020, at the national level and for states and territories. RESULTS: The real-world performance of public mental health services varied across the eight states and territories of Australia. Western Australia had the longest acute hospital stays and the lowest rates of involuntary admissions. Queensland (QLD) had the shortest acute hospital stays at the lowest cost. While the Australian Capital Territory had the highest rates of community treatment at the lowest cost, the Northern Territory had highest hospital and community costs with the most involuntary admissions. Victoria (VIC) had the lowest population percentage receiving specialised mental health services, the highest readmission rates after 28 days, and highest physical and mechanical restraint rates. CONCLUSIONS: The KPIs indicate that some states and territories show deviations from national benchmarks that may be important for consumers, carers and clinicians. For further improvement in quality and efficiency, more detailed contextual information is required, including detailed mapping of services.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".