Australian community and inpatient general public sector mental health services between 2017–18 and 2021–22: Relative stasis in bed capacity, increasing outpatient demand, and stunted expenditure
Bibliographic record
Abstract
OBJECTIVE: To descriptively analyse Australian public sector General Mental Health Services (GMHS) expenditure, ambulatory, and inpatient services, including key performance indicators (KPIs) in comparison with other subspeciality mental health services (MHS). METHOD: We descriptively analysed data published by the Australian Institute of Health and Welfare (AIHW), including inpatient, ambulatory services, expenditure, and KPIs. RESULTS: From 2017-18 to 2021-22, per capita expenditure for Australian GMHS (18-64) rose by an average annual inflation-adjusted change of 2%. Overall bed numbers remained static, with non-acute beds declining, and commensurate expansion of acute beds. Community GMHS had high outpatient utilisation, with high rates of schizophrenia, schizoaffective disorder, and bipolar affective disorders as primary diagnoses in mid-life. From 2017-18 to 2021-22, GMHS inpatient and ambulatory episodes had decreasing rates of significant improvement and increasing rates of significant deterioration. CONCLUSIONS: Although GMHS has the highest overall population and service utilisation, there has been static bed availability and relatively small increases in expenditure which are occurring concurrently with worsening clinical outcomes. Evidence-based modelling of GMHS and outcomes is required to inform future service improvement.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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 | 0.000 |
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".