Content Validity for a Child Care Self-assessment Tool: Creating Healthy Eating Environments Scale (CHEERS)
Bibliographic record
Abstract
The purpose of this project was to develop and content validate both a formative and summative self-assessment scale designed to measure the nutrition and physical activity environment in community-based child care programs. The study followed a mixed-method modified Ebel procedure. An expert group with qualifications in nutrition, physical activity, and child care were recruited for content validation. The survey was subjected to expert review through digital communication followed by a face-to-face validation meeting. To establish consensus for content validity beyond the standard error of proportion (P < 0.05) the content validity index (CVI) required was ≥0.78. Of the initial 64 items, 44 scored an acceptable CVI for inclusion. The remaining items were discussed, missing concepts identified, and a final CVI employed to determine inclusion. The final tool included 62 items with 5 subscales: food served, healthy eating program planning, healthy eating environment, physical activity environment, and healthy body image environment. Content validation is an integral step in scale development that is often overlooked or poorly carried out. Initial content validity of this scale has been established and will be of value to researchers and practitioners interested in conducting healthy eating interventions in child care.
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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 | 0.034 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".