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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".