<i>Evaluation of a Nutrition Education Component</i> Nested in the NutriSTEP™ Project
Bibliographic record
Abstract
PURPOSE: Parents' health literacy skills include food and nutrition knowledge, as well as the ability to read, comprehend, and use information related to their children's health. The evaluation of a nutrition education booklet within the NutriSTEP (Nutrition Screening Tool for Every Preschooler) Project was conducted. Parents' nutrition education needs and their sources of nutrition information were also assessed. METHODS: Eight dietitians from four provinces conducted in-person interviews with a sample of 322 parents. Parents were asked their perception of the booklet and reported learning. Dietitians' written feedback on the booklet and their recorded comments and nutrition advice to parents were gathered. RESULTS: Collated feedback led to significant revisions to the booklet. Parents reported increased knowledge from the booklet; 38% wanted more information on nutrition, while 25% wanted to know more about preschoolers and physical activity. The top three sources of nutrition advice for this parent sample were physicians, dietitians, and public health units. CONCLUSIONS: Written materials must be evaluated with the target audience to improve readability and comprehension. Further nutrition education efforts should be targeted through parents' main sources of nutrition information. Further research is needed on nutrition education intervention effectiveness to promote positive health outcomes.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".