Private psychiatric hospital care in Australia: a descriptive analysis of casemix and outcomes
Bibliographic record
Abstract
OBJECTIVE: To provide a rapid clinical update on casemix, average length of stay, and the effectiveness of Australian private psychiatric hospitals. METHODS: We conducted a descriptive analysis of the publicly available patient data from the Australian Private Hospitals Association Private Psychiatric Hospitals Data Reporting and Analysis Service website, from 2015-2016 to 2019-2020. This was compared with corresponding reporting on public and private hospitals from the Australian Institute of Health and Welfare, and Australian Mental Health Outcomes and Classification Network. RESULTS: In 2019-2020, there were 72 private psychiatric hospitals in Australia with 3582 acute beds. There were 42,942 inpatients with 1,286,470 days of care, and a mean length of stay 19.6 days (SD 13.9) for the financial year 2019-2020. The main diagnoses were major affective and other mood disorders (49%), and alcohol and other substance abuse disorders (21%). Clinician-rated outcome measures, that is, the HoNOS, showed an improvement effect size of 1.64, while the patient-rated MHQ-14 showed an improvement effect size of 1.18. Results are similar for previous years. CONCLUSIONS: Private psychiatric hospitals provide substantial, effective psychiatric care.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".