An audit of the dissemination strategies and plan included in international food-based dietary guidelines
Bibliographic record
Abstract
OBJECTIVE: Food-based dietary guidelines (FBDG) are an important resource to improve population health; however, little is known about the types of strategies to disseminate them. This study sought to describe dissemination strategies and content of dissemination plans that were available for FBDG. DESIGN: A cross-sectional audit of FBDG with a published English-language version sourced from the United Nations FAO repository. We searched for publicly available dissemination strategies and any corresponding plans available in English language. Two authors extracted data on strategies, which were grouped according to the Model for Dissemination Research Framework (including source, audience, channel and message). For guidelines with a dissemination plan, we described goals, audience, strategies and expertise and resources according to the Canadian Institute for Health Research guidance. SETTING: 18, 34 %) areas were included. PARTICIPANTS: n/a. RESULTS: The source of guidelines was most frequently health departments (79·2 %). The message included quantities and types of foods, physical activity recommendations and 88·7 % included summarised versions of main messages. The most common channels were infographics and information booklets, and the main end-users were the public. For twelve countries (22·6 %), we were able to source an English-language dissemination plan, where none met all recommendations outlined by the Canadian Institute for Health Research. CONCLUSIONS: The public was the most frequently identified end-user and thus most dissemination strategies and plans focused on this group. Few FBDG had formal dissemination plans and of those there was limited detailed provided.
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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 | 0.334 | 0.591 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".