Applying the Behavior Change Technique Taxonomy to Four Multicomponent Childhood Obesity Interventions
Bibliographic record
Abstract
Applying the Behavior Change Technique Taxonomy has the potential to facilitate identification of effective childhood obesity intervention components. This article evaluates the feasibility of coding Childhood Obesity Prevention and Treatment Consortium interventions and compares reliability between external taxonomy-familiar coders and internal intervention-familiar coders. After training, coder pairs independently coded prespecified portions of intervention materials. An adjudication process was used to explore coding discrepancies. Reliability between internal and external coders was moderate (prevalence and bias-adjusted kappa .38 to .55). Reliability for specific target behaviors varied with substantial agreement for physical activity (.63 to .76) and moderate for dietary intake (.44 to .63). Applying the taxonomy to these interventions was feasible, but agreement was modest. Coding discrepancies highlight the importance of refining coding to capture the complexities of childhood obesity interventions, which often engage multiple recipients (e.g., parents and/or children) and address multiple behaviors (e.g., diet, physical activity, screen time).
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".