Valid and Reliable Measure of Adherence to Satter Division of Responsibility in Feeding
Bibliographic record
Abstract
OBJECTIVE: To examine the validity and psychometrics of sDOR.2-6y, a 12-item measure of adherence to the Satter Division of Responsibility in Feeding (sDOR). DESIGN: Cross-sectional survey. SETTING: Online respondents in central Pennsylvania. PARTICIPANTS: 117 parents (94% female, 77% White, 62% in ≥1 income-based assistance program) of preschoolers aged 2-6 years (28% moderate/high nutrition risk). MAIN OUTCOME MEASURES: The sDOR.2-6y and Nutrition Screening Tool for Every Preschooler (NutriSTEP), a measure of child nutrition risk and other validated measures of eating behavior and parent feeding practices. ANALYSIS: Relationships were evaluated with Pearson r, t tests, ANOVA, or chi-square. Factor structure was investigated using principal components analysis with varimax rotation. Binary logistic regression and general linear model controlling for low-income status compared with sDOR.2-6y and NutriSTEP scores. Linear regression predicted NutriSTEP and Satter Eating Competence Inventory 2.0 scores from sDOR.2-6y. RESULTS: The sDOR.2-6y ranged from 16-32 (mean, 25.9 ± 3.3; n = 114). Parents of youth at nutrition risk had lower sDOR.2-6y scores (P = 0.004). Each 1 point sDOR.2-6y increase decreased nutrition risk odds by 21% (95% confidence interval, 0.675-0.918; P = 0.002). The sDOR.2-6y scores were higher with less restriction and pressure to eat (both P < 0.001) and were associated with feeding style. Specificity was 87% with sDOR.2-6y cutoff ≥24; sensitivity was 66% with cutoff ≥26. CONCLUSIONS AND IMPLICATIONS: The sDOR.2-6y accurately and reliably indicated adherence of low-income mothers to sDOR. Larger, diverse samples for future studies are recommended.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".