Beyond BMI: a feasibility study implementing NutriSTEP in primary care practices using electronic medical records (EMRs)
Bibliographic record
Abstract
INTRODUCTION: Primary care providers have a role to play in supporting the development of healthy eating habits, particularly in a child's early years. This study examined the feasibility of implementing the NutriSTEP® screen-a 17-item nutrition risk screening tool validated for use with both toddler and preschooler populations-integrated with an electronic medical record (EMR) in primary care practices in Ontario, Canada, to inform primary care decision-making and public health surveillance. METHODS: Five primary care practices implemented the NutriSTEP screen as a standardized form into their EMRs. To understand practitioners' experiences with delivery and assess factors associated with successful implementation, we conducted semi-structured qualitative interviews with primary care providers who were most knowledgeable about NutriSTEP implementation at their site. We assessed the quality of the extracted patient EMR data by determining the number of fully completed NutriSTEP screens and documented growth measurements of children. RESULTS: Primary care practices implemented the NutriSTEP screen as part of a variety of routine clinical contacts; specific data collection processes varied by site. Valid NutriSTEP screen data were captured in the EMRs of 80% of primary care practices. Approximately 90% of records had valid NutriSTEP screen completions and 70% of records had both valid NutriSTEP screen completions and valid growth measurements. CONCLUSION: Integration of NutriSTEP as a standardized EMR form is feasible in primary care practices, although implementation varied in our study. The application of EMR-integrated NutriSTEP screening as part of a comprehensive childhood healthy weights surveillance system warrants further exploration.
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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".