Policy, Structural Change and Quality of Psychiatric Services in Australia: The Views of Psychiatrists
Bibliographic record
Abstract
OBJECTIVE: Given that 10 years have elapsed since the implementation of Australia's National Mental Health Strategy, the aim of the paper was to ascertain the views of the country's psychiatrists about changes in mental health services. METHODS: A survey was mailed to all Fellows of the Royal Australian and New Zealand College of Psychiatrists living in Australia; 1039 out of 2059 (50%) returned the questionnaire. RESULTS: Private care has not changed much in the last 5 years, but the quality in public psychiatric services has deteriorated. While 67% of private practitioners, 46% of psychiatrists with mixed practice, 39% of exclusively public psychiatrists and 27% of academics believed public practice had deteriorated, only 18% of psychiatrist administrators believed that to be the case. Daily or weekly problems admitting patients to hospital was reported by 40% of psychiatrists working in the public system. Public psychiatrists believed that they now treat more patients who are more disturbed, more acute and more demanding. However, they see their patients less often, provide less psychotherapy and use more medication. Administrative demands have increased. CONCLUSIONS: According to psychiatrists, implementation of the National Mental Health Strategy has not yet resulted in better psychiatric care in the public health system.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".