NutriSTEP<sup>®</sup>is Reliable for Internet and Onscreen Use
Bibliographic record
Abstract
PURPOSE: NutriSTEP(®) screens for nutritional risk in preschoolers (3-5 years of age). Availability has been limited to paper versions. The objective is to test reliability for Internet and Onscreen use. METHODS: Two studies were conducted with parents in several Ontario Early Years Centres (Internet (n = 63)) and in the community and schools in Timmons, Guelph, and Ottawa, Ontario (Onscreen (n = 64)). Parents completed NutriSTEP(®) either on paper or using Internet or Onscreen versions. Two weeks later, the alternate mode was completed. Reliability was assessed using Intraclass Correlations (ICC) and Pearson Correlations (PC) on total and attribute scores, Kappa coefficients (κ) for risk, and Wilcoxon Signed Rank Test for responses on individual questions. RESULTS: For total scores, Internet and Onscreen ICCs were 0.94 and 0.91, respectively, with PCs of 0.89 and 0.84, respectively. Attribute scores were 0.69-0.91 (ICC) and 0.70-0.84 (PC) for Internet, and 0.81-0.92 (ICC) and 0.68-0.85 (PC) for Onscreen. κ amongst risk categories was 0.58 (P = 0.000) for Internet and 0.50 (P = 0.000) for Onscreen. For individual dichotomized questions, 5 of 17 (Onscreen and Internet) were excellent (κ > 0.75); 11 of 17 (Internet) and 9 of 17 (Onscreen) were adequate (0.40 < κ > 0.75); 0 of 17 (Internet) and 2 of 17 (Onscreen) questions were poor (κ < 0.4) in agreement between modes. CONCLUSIONS: Internet and Onscreen versions of NutriSTEP(®) are reliable.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".