Pilot Evaluation of an In-Store Nutrition Label Education Program
Bibliographic record
Abstract
PURPOSE: To describe and provide recommendations for the implementation of an evaluation for an already existing, in-store Nutrition Label Education Program (NLEP). METHODS: We describe the development and implementation of an evaluation consisting of a pre- and postsurvey and one month follow-up. The evaluation was designed to assess satisfaction with the NLEP as well as changes in participant nutrition label knowledge, confidence in using nutrition labels, and actual changes in nutrition label use. RESULTS: Nineteen participants took part in the pilot evaluation. The evaluation was successful in demonstrating high levels of satisfaction with the NLEP as well as positive changes in participant confidence and some increased knowledge in using nutrition labels. However, only 3 people participated in the follow-up, limiting the ability to assess behaviour change. CONCLUSIONS: Ideally, NLEPs should include ongoing evaluation that extends beyond just assessing participant satisfaction. Recommendations are provided for conducting such evaluations, including the importance of incorporating the evaluation into the program itself, using existing questionnaires when possible, and employing pre- and postsurveys as well as follow-up interviews to assess change.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".