Mental disorders and distress: Associations with demographics, remoteness and socioeconomic deprivation of area of residence across Australia
Bibliographic record
Abstract
OBJECTIVES: Australian policy-making needs better information on socio-geographical associations with needs for mental health care. We explored two national surveys for information on disparities in rates of mental disorders and psychological distress. METHODS: Secondary data analysis using the 2011/2012 National Health Survey and 2007 National Survey of Mental Health and Wellbeing. Key data were the Kessler 10 scores in adults in the National Health Survey (n = 12,332) and the National Survey of Mental Health and Wellbeing (n = 6558) and interview-assessed disorder rates in the National Survey of Mental Health and Wellbeing. Estimation of prevalence of distress and disorders for sub-populations defined by geographic and socioeconomic status of area was followed by investigation of area effects adjusting for age and gender. RESULTS: Overall, approximately one person in 10 reported recent psychological distress at high/very-high level, this finding varying more than twofold depending on socioeconomic status of area with 16.1%, 13.3%, 12.0%, 8.4% and 6.9% affected in the most to least disadvantaged quintiles, respectively, across Australia in 2011/2012. In the most disadvantaged quintile, the percentage (24.4%) with mental disorders was 50% higher than that in the least disadvantaged quintile (16.9%) in 2007, so this trend was less strong than for Kessler10 distress. CONCLUSION: These results suggest that disparities in mental health status in Australia based on socioeconomic characteristics of area are substantial and persisting. Whether considering 1-year mental disorders or 30-day psychological distress, these occur more commonly in areas with socioeconomic disadvantage. The association is stronger for Kessler10 scores suggesting that Kessler10 scores behaved more like a complex composite indicator of the presence of mental and subthreshold disorders, inadequate treatment and other responses to stressors linked to socioeconomic disadvantage. To reduce the observed disparities, what might be characterised as a 'Whole of Government' approach is needed, addressing elements of socioeconomic disadvantage and the demonstrable and significant inequities in treatment provision.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".