The National Survey of Mental Health and Well-Being in Australia: Impact on Policy
Bibliographic record
Abstract
OBJECTIVE: To provide a synopsis of the 3-part National Survey of Mental Health and Well-Being in Australia and to examine the yield in terms of policy and other changes in mental and general health services. METHOD: Published data are examined, and a commentary is provided on service-delivery issues that the data have revealed. RESULTS: One-year prevalence estimates for the common mental disorders, defined according to ICD-10 criteria and assessed using the automated version of the Composite International Diagnostic Interview (CIDI-A), have indicated rates similar to those of other countries (17.7%). Alarmingly high rates were found for alcohol and substance abuse in young persons, especially among young men. The number of years of life lost owing to disability attributable to mental disorders exceeds the number lost owing to cardiovascular disease and cancer. Only 35% of persons with 1 or more of the common mental disorders had sought help in the 12 months prior to interview. The point prevalence for mental health problems was 14% for persons aged 4 to 17 years. The point prevalence for psychotic disorders was 4.7 per 1000. An encouraging finding is that 81% of affected individuals had been to their general practitioner (GP) in the last year. However, only 20% had participated in any rehabilitation program in the past year. CONCLUSIONS: The Survey results are based on a national population sample, not on individuals reaching services. They have therefore proved to be of great value in influencing policy at federal and state levels and may have contributed to increased funding for both services and research.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".