Trends in physical and mental health needs across generations in Australia
Bibliographic record
Abstract
Despite evidence of changes in age-specific incidence and prevalence rates for chronic conditions and disease, future health resource planning is often based on historical age- and gender-specific service use, neglecting changes in the need for care within age groups between generations. This paper studies differences in health needs by age and gender across birth cohorts in Australia and considers the implications for future health service planning. Whilst controlling for age and period effects, we find that more recent-born female birth cohorts have higher prevalence rates of long-term health conditions than earlier-born cohorts, whereas we don't find an effect for males. The increase for females corresponds with an increase in probable mental disorders, and while we also find an increase in probable mental disorders among males, decreases in physical impairment rates among both genders offset the overall rates of long-term health conditions among males but not among females, where increases in probable mental disorders are larger. Comparing projections of mental health service requirements that integrate cohort effects, as opposed to those that do not, shows that traditional planning models may underestimate health service requirements for the future. Our findings suggest that health service planners should relax assumptions about constant age-specific use.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".