Perceived need for mental health care, findings from the Australian National Survey of Mental Health and Well-being
Bibliographic record
Abstract
BACKGROUND: The Australian National Survey of Mental Health and Well-being was designed to detect and describe psychiatric morbidity, associated disability, service use and perceived need for care. The survey employed a single-phase interview methodology, delivering a field questionnaire to a clustered probability sample of 10,641 Australians. Perceived need was sampled with an instrument designed for this survey, the Perceived Need for Care Questionnaire (PNCQ). This questionnaire gathers information about five categories of perceived need, assigning each to one of four levels of perceived need. Reliability and validity studies showed satisfactory performance of the instrument. METHODS: Perceived need for mental health care in the Australian population has been analysed using PNCQ data, relating this to diagnostic and service utilization data from the above survey. RESULTS: The survey findings indicate that an estimated 13.8% of the Australian population have perceived need for mental health care. Those who met interview criteria for a psychiatric diagnosis and also expressed perceived need make up 9.9% of the population. An estimated 11.0% of the population are cases of untreated prevalence, a minority (3.6% of the population) of whom expressed perceived need for mental health care. Among persons using services, those without a psychiatric diagnosis based on interview criteria (4.4% of the population), showed high levels of perceived met need. CONCLUSIONS: The overall rate of perceived need found by this methodology lies between those found in the USA and Canada. The findings suggest that service use in the absence of diagnosis elicited by survey questionnaires may often represent successful intervention. In the survey, untreated prevalence was commonly not accompanied by perceived need for mental health care.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".