Bibliographic record
Abstract
This lecture argues that mental health is a major factor of production. It is the biggest single influence on life satisfaction, with mental health eight years earlier a more powerful explanatory factor than current income. Mental health also affects earnings and educational success. But, most strikingly, it affects employment and physical health. In advanced countries mental health problems are the main illness of working age - amounting to 40% of all illness under 65. They account for over one third of disability and absenteeism in advanced countries. They can also cause or exacerbate physical illness. It is estimated that in the absence of mental illness, the costs of physical healthcare for chronic diseases would be one third lower. The good news is that cost-effective treatments for the most common mental illnesses now exist (both drugs and psychological therapy). But only a quarter of those who suffer are in treatment. Yet psychological therapy, such as cognitive behavioural therapy, if more widely available would pay for itself in savings on benefits and lost taxes. The lecture ends by illustrating how rational policy can be made using life-course models of wellbeing. Such policies should include a much greater role for the treatment and prevention of mental illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".