The Development and Refinement of an e-Health Screening, Brief Intervention, and Referral to Treatment for Parents to Prevent Childhood Obesity in Primary Care
Bibliographic record
Abstract
BACKGROUND: Nearly one-third of Canadian children can be categorized as overweight or obese. There is a growing interest in applying e-health approaches to prevent unhealthy weight gain in children, especially in settings that families access regularly. Our objective was to develop and refine an e-health screening, brief intervention, and referral to treatment (SBIRT) for parents to help prevent childhood obesity in primary care. MATERIALS AND METHODS: Our SBIRT, titled the Resource Information Program for Parents on Lifestyle and Education (RIPPLE), was developed by our research team and an e-health intervention development company. RIPPLE was based on existing SBIRT models and contemporary literature on children's lifestyle behaviors. Refinements to RIPPLE were guided by feedback from five focus groups (6-10 participants per group) that documented perceptions of the SBIRT by participants (healthcare professionals [n = 20], parents [n = 10], and researchers and graduate trainees [n = 8]). Focus group commentaries were transcribed in real time using a court reporter. Data were analyzed thematically. RESULTS: Participants viewed RIPPLE as a practical, well-designed, and novel tool to facilitate the prevention of childhood obesity in primary care. However, they also perceived that RIPPLE may elicit negative reactions from some parents and suggested improvements to specific elements (e.g., weight-related terms). CONCLUSIONS: RIPPLE may enhance parents' awareness of children's weight status and motivation to change their children's lifestyle behaviors but should be improved prior to implementation. Findings from this research directly informed revisions to our SBIRT, which will undergo preliminary testing in a randomized controlled trial.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".