Developing and Validating the Preschool Nutrition Education Practices Survey
Bibliographic record
Abstract
OBJECTIVE: Validate the Preschool Nutrition Education Practices Survey. DESIGN: Iterative approach combining design-based research and Standards for Educational and Psychological Testing. SETTING: Los Angeles, CA and Philadelphia, PA Early Care and Education (ECE) classrooms. PARTICIPANTS: Expert panel members (n = 7); ECE teachers: interviews (n = 8), pilot survey (n = 31), and final survey (n = 136). VARIABLES MEASURED: Early care and education nutrition education practices used in the classroom either during class time or mealtime. ANALYSIS: Qualitative content analysis was implemented for content, response process, and consequences of testing validity evidence. Rasch rating scale analysis was conducted for the response process and internal structure validity and reliability evidence. RESULTS: Qualitative field-testing produced strong content, response process, and consequences of testing validity evidence to inform survey modifications. Quantitative field-testing generated a psychometrically sound, well-targeted 12-item survey on a 4-point frequency scale with excellent item and person reliability (0.97 and 0.93 respectively) and separation (5.36 and 3.77 respectively); good Rasch Principal Components Analysis findings (60.3%); and productive item fit statistics (0.50-1.50 logits). CONCLUSIONS AND IMPLICATIONS: Robust validity (content, response process, consequences of testing, internal structure) and reliability evidence were demonstrated for using the Preschool Nutrition Education Practices Survey to assess ECE teachers' use of nutrition education practices. Future research is needed to examine its relationship to other variables, such as nutrition teaching efficacy, and to determine its ability to detect change in ECE nutrition education practices over time and across groups.
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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 | 0.080 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".