Young Australians with moderate to severe mental health problems: client data and outcomes at Children and Young People's Mental Health
Bibliographic record
Abstract
Aim: Almost a quarter of young Australians experience a mental health issue that may become chronic if left untreated. Children and Young People's Mental Health (CYPMH) is a specialist tertiary service for young people with moderate to severe mental health problems on the Central Coast in Australia. This paper presents an overview of client data and service use collected over a 1 year period specific to the Youth Mental Health (YMH) component of the service. Method: Client data, including demographic characteristics, service usage, presenting issues and standardized outcome measures, were analysed using SPSS. Clinicians routinely collect MH-OAT (Mental Health Outcomes and Assessment Tools) measures at different points in a client's episode of care, and each of these measures was analysed separately. Wilcoxon Z and a series of McNemar's tests were used to report on the difference between admission and discharge scores. Results: During a designated 1 year period, 830 referrals to YMH were received. The most prevalent presenting issue was suicidal ideation followed by deliberate self-harm and depression. A comparison of admission and discharge outcome scores shows significant improvement by discharge on a range of measures. Specifically, analysis identified significant differences between admission and discharge HoNOSCA (Health of the Nation Outcomes Scales for Children and Adolescents) and CGAS (Children's Global Assessment Scale) scores for young people aged 12-17 and HONOS (Health of the Nation Outcomes Scale) scores for young people aged 18-24. Conclusion: The clinical outcomes for young people are positive with improvements seen on a range of measures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".