Bibliographic record
Abstract
Abstract The health of populations is related to features of society and its social and economic organization. This crucial fact provides the basis for effective policy-making for improving population health. While there is, understandably, much concern regarding the appropriate provision and financing of health services as well as ensuring that the nature of the services provided is based on the best evidence of effectiveness, health is a matter that goes beyond the provision of health services. Policies pursued by many branches of government and by the private sector, both nationally and locally, exert a powerful influence on health — and this book shows the direction in which we should be going. Just as decisions about health services should be based on the best evidence available, so should policies related to the social determinants of health. The social determinants covered by the book include the impact of early life; the life course, the social gradient, and health; labour market disadvantage, unemployment, non-employment, and job insecurity; the psychosocial environment at work; transport; social support and social cohesion; the politics of food; poverty, social exclusion, and minorities; social patterning of individual behaviours; social determinants of ethnic/ racial inequalities; social determinants of health in older age; neighbourhoods, housing, and health; sexual behaviour and sexual health; and social vulnerability.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".