Bibliographic record
Abstract
In this brief primer on prevention, the authors raise some of the scientific, social, behavioural, political and practical issues that must be addressed to integrate effective preventive initiatives into our health system (which includes public health practice). They begin by reviewing the contributions science has and has not made to inform our prevention efforts. The authors further examine what it is we know about changing human behaviour in health-promoting ways. The article closes with a review of the practical challenges for prevention-oriented policies and programs in the health system and in society as a whole. The authors call for increased emphasis on strategies that encourage the creation of supportive environments. Moreover, they identify that we need to try for fewer, but better-thought-out and more sustained, multi-level health promotion and disease prevention interventions. Community-led interventions that identify, address and change local cultural norms that contribute to these health concerns are especially key. Finally, the authors identify the need for rigorous evaluation to ensure that all effects, good and bad, of preventive interventions are fully captured and addressed.
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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".