Diffusion and dissemination of evidence-based dietary srategies for the prevention of cancer
Bibliographic record
Abstract
OBJECTIVE: The purpose was to determine what strategies have been evaluated to disseminate cancer control interventions that promote the uptake of adult healthy diet? METHODS: A systematic review was conducted. Studies were identified by searching MEDLINE, PREMEDLINE, Cancer LIT, EMBASE/Excerpta Medica, PsycINFO, CINAHL, the Cochrane Database of Systematic Reviews, and reference lists and by contacting technical experts. English-language primary studies were selected if they evaluated the dissemination of healthy diet interventions in individuals, healthcare providers, or institutions. Studies of children or adolescents only were excluded. RESULTS: One hundred one articles were retrieved for full text screening. Nine reports of seven distinct studies were included; four were randomized trials, one was a cohort design and three were descriptive studies. Six studies were rated as methodologically weak, and one was rated as moderate. Studies were not meta-analyzed because of heterogeneity, low methodological quality, and incomplete data reporting. No beneficial dissemination strategies were found except one that looks promising, the use of peer educators in the worksite, which led to a short-term increase in fruit and vegetable intake. CONCLUSIONS AND IMPLICATIONS: Overall, the quality of the evidence is not strong and is primarily descriptive rather 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 with different messages and different target 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 | 0.136 | 0.332 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 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".