Eating-Related and Psychological Outcomes of Health at Every Size Intervention in Health and Social Services Centers Across the Province of Québec
Bibliographic record
Abstract
PURPOSE: To report the outcomes of a Health at Every Size (HAES) intervention in a real-world setting. DESIGN: Quasi-experimental design evaluating eating behaviors and psychological factors. SETTING: The HAES intervention is offered in Health and Social Services Centers in Québec (Canada). PARTICIPANTS: ) from a comparison group. INTERVENTION: The HAES intervention is composed of 14 weekly meetings provided by health professionals. It focuses on healthy lifestyle, self-acceptance, and intuitive eating. MEASURES: Eating behaviors (ie, flexible restraint, rigid restraint, disinhibition, susceptibility to hunger, intuitive eating, and obsessive-compulsive eating) and psychological correlates (ie, body esteem, self-esteem, and depression) were assessed using validated questionnaires at baseline, postintervention, and 1-year follow-up. ANALYSIS: Group, time, and interaction effects analyzed with mixed models. RESULTS: Significant group by time interactions were found for flexible restraint ( P = .0400), disinhibition ( P < .0001), susceptibility to hunger ( P < .0001), intuitive eating ( P < .0001), obsessive-compulsive eating ( P < .0001), body-esteem ( P < .0001), depression ( P = .0057), and self-esteem ( P < .0001), where women in the HAES group showed greater improvements than women in the comparison group at short and/or long term. CONCLUSION: The evaluation of this HAES intervention in a real-life context showed its effectiveness in improving eating-, weight-, and psychological-related variables among women struggling with weight and body image.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".