Enhancing nutrition education resources through the development and refinement of a checklist using the suitability assessment of materials (SAM)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it