“C.H.A.M.P. Families”: Description and Theoretical Foundations of a Paediatric Overweight and Obesity Intervention Targeting Parents—A Single-Centre Non-Randomised Feasibility Study
Bibliographic record
Abstract
Childhood obesity represents a significant global health challenge, and treatment interventions are needed. The purpose of this paper is to describe the components and theoretical model that was used in the development and implementation of a unique parent-focussed paediatric overweight/obesity intervention. C.H.A.M.P. Families was a single-centre, prospective intervention offered to parents of children aged between 6⁻14 years with a body mass index (BMI) ≥85th percentile for age and sex. The intervention included: (1) eight group-based (parent-only) education sessions over 13-weeks; (2) eight home-based activities; and (3) two group-based (family) follow-up support sessions. The first section of the manuscript contains a detailed description of each intervention component, as well as an overview of ongoing feasibility analyses. The theoretical portion details the use of evidence-based group dynamics principles and motivational interviewing techniques within the context of a broader social cognitive theory foundation. This paper provides researchers with practical examples of how theoretical constructs and evidence-based strategies can be applied in the development and implementation of parent-focussed paediatric obesity interventions. Given the need for transparent reporting of intervention designs and theoretical foundations, this paper also adds to the areas of implementation science and knowledge translation research.
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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 | 0.021 | 0.019 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".