Health At Every Size intervention® under real-world conditions: the rights and wrongs of program implementation
Bibliographic record
Abstract
Implementation integrity is known to be critical to the success of interventions. The Health At Every Size® (HAES®) approach is deemed to be a sustainable intervention on weight-related issues. However, no study in the field has yet investigated the effects of implementation on outcomes in a real-world setting.Objective This study aims to explore to what extent does implementation integrity moderate program outcomes across multiple sites.Methods One hundred sixty-two women nested in 21 health facilities across the province of Québec (Canada) were part of a HAES® intervention and completed questionnaires at baseline and after the intervention. Participant responsiveness (e.g. home practice completion) along with other implementation dimensions (dosage, adherence, adaptations) and providers’ characteristics (n = 45) were assessed using a mix of qualitative and quantitative data analysis. Adaptations to the program curriculum were categorized as either acceptable or unacceptable. Multilevel linear modeling was performed with participant responsiveness and other implementation dimensions predictors. Intervention outcomes were intuitive eating and body esteem.Results Unacceptable adaptations were significantly associated with providers’ self-efficacy (rs(23) = .59, p = .003) and past experience with facilitating the intervention (r(23) = .47, p = .03). Participant responsiveness showed a significant interaction between time and home practice completion (B = .07, p < .05) on intuitive eating scores.Conclusion Except for participant responsiveness, other implementation dimensions did not moderate outcomes. Implications for future research and practice are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".