Mapping the regional and remote specialised mental health workforce: Commentary on the AIHW data for 2022–2023
Bibliographic record
Abstract
ObjectiveThe Australian Institute of Health and Welfare publishes statistical indicator reports on the specialised mental health workforce. These include data for 2022-2023 on psychiatrists, mental health nurses, mental health occupational therapists, psychologists and mental health social workers. We provide a brief commentary on these reports, reflecting upon the implications of such changes for psychiatric practice and patient care.ConclusionsOverall, there are fewer mental health workers with increasing distance from urban centres. There are insufficient rural psychiatrists with the NT and Queensland having higher rates per 100,000 in outer regional and remote areas. Psychologists and mental health nurses have the highest rates per 100,000 in rural areas. Though low in absolute rates per 100,000, mental health social workers are better distributed in rural compared to urban areas. Further data on public, private and non-governmental sector employment would be useful.
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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 | 0.035 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.038 | 0.046 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".