Bibliographic record
Abstract
While policy makers argue for a greater share of health resources to go to health promotion, action is stalled by, among other things, the perception that little is known about which interventions offer the best health returns. Additionally, what is missing is any sense of what the economic literature in health promotion looks like overall. Where is the economic evidence plentiful and where is it scant? The project described here compiled a census of economic evaluations in health promotion. Studies were classified according to a four-part typology that documented the strategic intent of the intervention, the risk factor being addressed, the population most affected and the setting in which the intervention took place. Since 1990, there have been over 400 economic evaluations of health-promoting interventions in the peer review and grey literatures. Of these, 90% address biological or behavioral determinants of health. Relatively little is known about the economics of population health advocacy or interventions to tackle the social and economic determinants of health. Initiatives are in place to increase the availability of economic evidence. Research is also needed into how to support decision makers' use of imperfect, incomplete and uncertain information.
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 | 0.038 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.027 | 0.046 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".