Evaluation of a Pediatric Obesity Management Toolkit for Health Care Professionals: A Quasi-Experimental Study
Bibliographic record
Abstract
Health care professionals (HCPs) play a critical role in helping to address weight-related issues with pediatric patients, yet often feel ill-equipped to discuss/manage this complex and sensitive health issue. Using the five As (“Ask, Assess, Advise, Agree, and Assist”) of Pediatric Obesity Management, we created a series of educational videos and evaluated the content, quality (acceptability, engagement), and impact of these videos on HCPs’ self-efficacy, knowledge, and change in practice when addressing weight-related issues with pediatric patients and their families using questionnaires. HCPs (n = 65) participated in a baseline assessment and 4–6 month follow-up (n = 54). Knowledge and self-efficacy increased post-video for the majority of participants. At follow-up, most HCPs reported a change in their practice attributable to viewing the videos, and their self-efficacy ratings improved over time for the majority of questions asked. Most participants rated aspects of each of the videos highly. Preliminary findings suggest that an evidence-based educational toolkit of videos, based on the 5As framework, may lead to changes in self-reported behaviors among HCPs, and sustained improvements in their self-efficacy in addressing weight-related topics with children and their families. (Clinical Trial Number NCT04126291).
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".