Bringing nutrition risk screening to preschoolers: Development of a Nutrition Risk Screening Tool for Every Preschooler (NutriSTEP <sup>™</sup> )
Bibliographic record
Abstract
What can you do about obesity and other nutrition problems in preschoolers? Early identification may prevent nutrition problems. This presentation will describe NutriSTEP ™ development (feasibility, construct and question development, refinement, validation, test‐retest reliability, and implementation) and how it can be used in the community. More than 1500 preschoolers and parents, including several cultural groups were involved. NutriSTEP ™ is valid and reliable; items address food and nutrient intake, physical growth, developmental and physical capabilities, physical activity, food security and feeding environment. A parent can complete the 17 questions in only five minutes. Based on prevalence in the validation study (n=269), approximately 10% of the preschool population are at “high risk” and need primary prevention. As part of a screening program, NutriSTEP ™ can direct ‘at risk” children to community resources. NutriSTEP ™ data can be used for planning programs and proposing areas for research and practice including nutrition education and effectiveness interventions. This overview will provide the audience with necessary knowledge of this new screening tool. Supported by: Canadian Institutes of Health Research, Ontario Public Health Research, Education and Development Program, City of Greater Sudbury, Ontario Early Years Challenge Fund, Health Canada (Population Health Fund)
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".