An exploration of the translation of MEND 5-7 for the BC context using the RE-AIM framework
Bibliographic record
Abstract
Introduction: Prevalence of obesity and overweight in children is growing in Canada and worldwide. In recent years multiple approaches have been utilized to treat childhood obesity. Multidisciplinary and family-based approaches have been shown to be successful in the management of obesity and overweight in children however, there is limited evidence about their use with younger children. Therefore the purpose of this thesis was to explore the translation of a family-based intervention, MEND 5-7, for the British Columbia (BC) context using the RE-AIM framework. Methods: A mixed methods design was used. Twenty-four children and their families who were enrolled in MEND program across 3 cities participated in the study. Anthropometrics and self-reported lifestyles and attitudes were measured for both children and parents pre and post program. MEND staff completed recruitment and registration tracking, and session feedback forms; were interviewed about implementation and meeting minutes were analysed for the process evaluation. In addition parent feedback surveys and site visit feedback forms were also included. Data were organized and analysed based on the RE-AIM framework. Results: The sample was too small to be representative but the demographics of the group appeared to be similar to the BC population. A number of significant improvements were seen related to physical activity (screen-time, p = .002; parent self-efficacy for increasing play and enjoyment, p = .05) and eating habits (parent fruit and vegetable consumption, p = .009). The small number of participants made in difficult to draw a valid conclusion about the changes in anthropometry. Data on implementation showed that families and group leaders were highly satisfied with the program. The program was found to be feasible and facilitators included team work, flexibility, participants’ engagement, community relations, preparation and planning, and support. Barriers to implementation included were time, recruitment, parental involvement, diversity, lack of flexibility in manual and location. Conclusion: MEND 5-7 appeared feasible for the BC population, however recruitment remains a significant challenge to program operations. Further research using an experimental design is needed.
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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 | 0.045 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".