Child and Adolescent Mental Health Services in Australia: A descriptive analysis between 2015–16 and 2019–20
Bibliographic record
Abstract
OBJECTIVE: To provide analysis and commentary on Australian state/territory child and adolescent mental health service (CAMHS) expenditure, inpatient and ambulatory structure and key performance indicators. METHOD: Data from the Australian Institute of Health and Welfare and the Australian Bureau of Statistics were descriptively analysed. RESULTS: Between 2015-16 and 2019-20, overall CAMHS expenditure increased by an average annual rate of 3.6%. Per capita expenditure increased at a higher rate than for other subspeciality services. CAMHS admissions had a higher cost per patient day, shorter length of stay, higher readmission rate and lower rates of significant improvement. Adolescents aged 12-17 had high community CAMHS utilisation, based on proportion of population coverage and number of service contacts. CAMHS outpatient outcomes were similar to other age-groups. There were high rates of 'Mental disorder not otherwise specified', depression and adjustment/stress-related disorders as principal diagnoses in community CAMHS episodes. CONCLUSIONS: CAMHS inpatient admissions had lower rates of significant improvement and higher 14-day readmission rates than other ages. Australia's young population had a high outpatient CAMHS contact rate. Evidence-based modelling of CAMHS providers and outcomes may inform future service improvement.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".