Validation of Toddler NutriSTEP®
Bibliographic record
Abstract
The objective was to establish criterion and construct validity for Toddler NutriSTEP®, a 17‐item, community‐based parent‐administered nutrition risk screening questionnaire (total score = 68), adapted from the original preschooler version. Parents of 200 toddlers (18–35 mo), recruited from community settings, completed Toddler NutriSTEP® and a demographic questionnaire; an assessment by a registered dietitian (RD) included medical and nutritional history, weight, height, 3‐d food records. The RD rated nutritional risk on a scale of 1 (low) to 10 (high). Receiver operating curves (ROC) were used to determine sensitivity and specificity for various cut‐points on the scale. Differences in Toddler NutriSTEP® scores for selected demographic variables were determined by ANOVA. Scores on Toddler NutriSTEP® and the RD rating were correlated (r=0.66, p<0.000). Areas under the ROC curves for the moderate risk (score 5–7) and the high risk (score 8+) RD ratings were 0.87 and 0.82 with cut points of >;20 & >;25, respectively. Scores on Toddler NutriSTEP® were significantly higher (more risk) for parents with low incomes, low education and for immigrants to Canada. The Toddler NutriSTEP® questionnaire demonstrates both criterion and construct validity in children 18 – 35 months. Funding: Canadian Institutes of Health Research
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".