Modeled estimates of the health outcomes and economic value of improving the social determinants of mental health
Bibliographic record
Abstract
Abstract The prevalence and burden of mental disorders have worsened despite increased community awareness. Enhanced access to treatments alone is unlikely to deliver improvements in population mental health, so more attention needs to be paid to social and environmental influences. Here we estimate the health benefits and economic value of improving the social determinants of mental health within Brisbane South, a diverse population of 1.2 million people, in Australia. The incremental net monetary benefit (combining costs and monetized health outcomes) derived from 5% improvements in the average yearly change of social cohesion, childhood difficulties, substance misuse and unemployment over 11 years from 2024 to 2034 was projected to be AUD$146.64 million, AUD$234.50 million, AUD$281.67 million and AUD$100.43 million, respectively. Quality-adjusted life years, suicide deaths, emergency department presentations and self-harm hospitalizations were also improved. This study demonstrates the health and economic value of investing in the social determinants of mental health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".