Enhancing nutrition education resources through the development and refinement of a checklist using the suitability assessment of materials (SAM)
Bibliographic record
Abstract
Background Evidence-based nutrition education resources are one way to help registered dietitians (RDs) translate scientific knowledge to consumers. Aim To develop a checklist based on suitability assessment of materials (SAM) and to assess its use to refine nutrition education resources. Methods RDs were recruited online to assess two nutrition education resources using SAM. Three rounds of surveying and two rounds of resource refinements occurred. A “checklist” was created to refine the resources between rounds. Descriptive statistics and nonparametric tests were performed to explore differences in SAM-scores between rounds. Results RDs participated in the first ( n = 45), second ( n = 37), and third ( n = 27) surveys. SAM-scores significantly improved in both resources by the third round. The refined checklist included more explicit instructions and provided examples to help guide resource changes. Conclusions Using the checklist improved SAM scores. Future work should include end-users to help with checklist validation.
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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 | 0.100 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".