Diffusion and Dissemination of Evidence-Based Dietary Strategies for the Prevention of Cancer
Bibliographic record
Abstract
We used a systematic review to identify strategies that have been evaluated for disseminating cancer control interventions that promote the uptake of a healthy diet in adults. Studies were identified by contacting technical experts and by searching MEDLINE, PreMedline, CANCERLIT, EMBASE/Excerpta Medica, PsycINFO, CINAHL, the Cochrane Database of Systematic Reviews, and reference lists. English-language primary studies were selected if they evaluated the dissemination of healthy diet interventions to individuals, health care providers, or institutions. Studies involving only children or adolescents were excluded. We retrieved 101 articles for full-text screening, and identified nine reports of seven distinct studies. Four of the studies were randomized trials, one was a cohort design, and three were descriptive studies. Six of the studies were rated methodologically weak, and one was rated moderate. Because of heterogeneity, low methodological quality, and incomplete data reporting, the studies were not pooled for meta-analysis. No beneficial dissemination strategies were found. One strategy involving the use of peer educators at the work site, which led to a shortterm increase in fruit and vegetable intake, looks promising. Overall, the quality of the evidence is not strong, and the evidence that exists is more descriptive than evaluative. No clear conclusions can be drawn from these data. Controlled studies are needed to evaluate dissemination strategies and to compare dissemination and diffusion strategies that communicate different messages and target different audiences.
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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 | 0.231 | 0.460 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.037 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".