Australian older persons mental health inpatient and ambulatory services in 2015–2020 – A descriptive analysis and commentary
Bibliographic record
Abstract
OBJECTIVE: To provide a commentary on Australian state/territory older persons mental health service (OPMHS) expenditure, inpatient and outpatient services and key performance indicators (KPIs). METHOD: Descriptive analysis of data from the Australian Institute of Health and Welfare (AIHW), the Australian Bureau of Statistics and the World Health Organisation. RESULTS: Between 2015-16 and 2019-20, annual expenditure on OPMHS in Australia increased by an average of only 2.3%, compared to 2.9% for all population groups, despite an increase in the number of over 65 year olds. Per capita recurrent expenditure on OPMHS decreased by an average of 1% annually. Australia's total mental health beds increased, whereas OPMHS beds decreased, mainly due to a reduction in non-acute beds. Outcomes for OPMHS admissions were similar to other age groups, except for a longer length of stay and reduced readmission rate. Older Australians accessed ambulatory mental health care at a lower rate and had a lower rate of improvement after a completed episode. CONCLUSIONS: OPMHS expenditure has not increased at commensurate levels compared to other populations. The mental health of people aged over 65 appears to be a neglected policy priority in Australia. The Royal Commission into Aged Care Quality and Safety may herald service and expenditure changes.
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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 | 0.020 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".