Creating a healthy eating and active environment survey (CHEERS) for childcare: an inter-rater, intra-rater reliability and validity study
Bibliographic record
Abstract
BACKGROUND: The CHEERS is a self-administered tool to measure gaps, weaknesses, and strengths of an early childhood education and care (ECEC) centre-based nutrition and physical activity environment. ECEC settings have the potential to profoundly influence early dietary and physical activity behaviours. Content validation of the CHEERS tool has been previously reported. The purpose of this study was to develop reliability and validity evidence for the CHEERS audit tool and the proposed subscales of food served, healthy eating environment, program planning, and physical activity environment in ECEC centre-based programs. METHODS: This cross-sectional study consisted of 2 phases: Phase 1 included inter-, intra-rater and Cronbach's α. A subset of this sample was invited to participate in a second survey (Trial 2) for intra-rater assessment within 3 weeks of completing the first survey (Trial 1). Phase 2 included concurrent validity assessment between a nutrition expert and the ECEC director using within a one-week period. RESULTS: One hundred two directors and 85 educators (total of 187) returned the survey. Of these, there were 75 matched pairs for inter-rater reliability analysis providing a CHEERS ICC score of 0.59 and ICC scores ranging from 0.40 to 0.58 for the subscales. The ICC for intra-rater reliability of the CHEERS score was 0.81 for 40 participants completing the survey a second time and a range of 0.72 to 0.79 for the subscales. The CHEERS tool demonstrated very good internal consistency (α = 0.91) and α scores ranging from 0.73 to 0.79 for the subscales. In phase 2, concurrent validation was ICC = 0.65 (n = 30) CHEERS scores with a range of 0.42 to 0.69 for the subscales. CONCLUSIONS: This study provides evidence of inter-, intra-rater reliability, internal consistency, and concurrent validity of an environmental assessment audit tool to assess the nutrition and physical activity environment of ECEC centre-based programs. The results demonstrate that the self-administered CHEERS instrument is stable overtime and between evaluators at the same ECEC centre. The scores obtained with CHEERS self-administered audit tool are reasonably accurate compared to an expert rater (dietitian) assessment. This study adds additional support to establishing the psychometric soundness of the CHEERS tool.
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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 | 0.059 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".