Bibliographic record
Abstract
Health promoters require credible evidence to identify relevant determinants of health, choose activities to promote health, and then evaluate the effectiveness of these chosen activities. The issue of evidence in health promotion is a complex one that requires critical examination of what is meant by health promotion, the focus of health promotion activities, and the ideological issues and principles that inform health promotion practice. It is argued that health promoters should be explicit about the principles and values behind their health promotion activities, and consider how ideology, values, principles and data interact to produce evidence. Resources that will provide assistance in these tasks are provided.
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 | 0.456 | 0.681 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.009 | 0.124 |
| Scholarly communication | 0.036 | 0.093 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.051 | 0.060 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".