The Implementation of a Blended In-Person and Online Family-Based Childhood Obesity Management Program: A Process Evaluation Pilot Study
Bibliographic record
Abstract
BACKGROUND: The Early Intervention Program (EIP) was a 10-week family-based healthy living intervention for children with a BMI-for-age ≥85th percentile. The effectiveness of the EIP has been previously demonstrated; however, its implementation has not been fully described. Process evaluations provide valuable insight into implementation and improve ongoing intervention delivery. OBJECTIVE: The aim was to evaluate recruitment, intervention content, delivery, and implementation for quality improvement and to inform potential scale-up. METHODS: A mixed-methods process evaluation design was used and represented one component of a Type I hybrid effectiveness trial. RESULTS: = 47). Participation barriers were transportation, scheduling, and illness. Participation facilitators were the free cost and family recreation pass, sibling inclusion, and location. Program acceptability/satisfaction was rated over 4/5 for all measures. Implementation barriers were recruitment, small group size, attendance, and limited time to deliver material. Implementation facilitators were high compatibility and feasibility, as well as the provided resources. Staff interviews showed high acceptability/satisfaction across all sites. CONCLUSION: The EIP was highly acceptable and feasible for families and delivery partners, but recruitment, attendance, and online engagement were implementation challenges. Program adjustments are recommended prior to scale-up. These strengths and limitations can help to inform other multi-site childhood healthy living interventions.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".