Bibliographic record
Abstract
paper, "Prevention: Delivering the Goods," with its focus on prevention, is very timely as current developments in infectious diseases, in particular SARS, bring the field of public health to the forefront.Excellence in public health practices is easily overlooked or taken for grantedyet, safe water, air, food and other environmental factors are essential to our survival.Frank and Di Ruggiero ask why prevention has been so slow to have a major effect.The authors are eminently qualified to answer such a question because of their wealth of experience and education in disease prevention and health promotion.As they point out, it is not exactly clear what is meant by "good" preventive practice.We all know from personal experiences how confusing expert opinion can be.For example, if we want to reduce our probability or risk of heart failure we are advised to do a combination of the following: eat healthily (which can be interpreted in many ways -lower fat intake, no red meat, eat lots of fruit and vegetables), lose weight, exercise regularly (varying advice about how much and what type), take aspirin daily and/or a wide variety of other dietary supplements.Add these probabilities together and follow the advice, we should all live to over 100, but we all know that is unlikely.Frank and Di Ruggiero attribute the lack of progress in prevention to our "premature enthusiasm" for new approaches and the constant search for a magic bullet.In terms of healthy behaviours, we may all have good intentions, but unfortunately we let things slide.The authors remind us how challenging it is to carry out simple preventive practices such as forgetting to floss or brush our teeth..Even more difficult is trying to change a behaviour such as smoking, even though the evidence against it is clear.Prevention may be better than cure, but we are all vulnerable to effects of genetics and social circumstances.Frank and Di Ruggiero stress the importance of opening up a public dialogue if we want large-scale prevention to work.They make a case for multiple interventions, at both the individual and community levels, to make the culture more receptive to change.We all have a great deal to learn about how to make the case for prevention stick.
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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.449 | 0.186 |
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".