Psychological Impact of a “Health-at-Every-Size” Intervention on Weight-Preoccupied Overweight/Obese Women
Bibliographic record
Abstract
The aim of the present study was to assess the impact of a "Health-at-every-size" (HAES) intervention on psychological variables and body weight the weight-preoccupied overweight/obese women. Those women were randomized into three groups (1) HAES, (2) social support (SS), (3) waiting-list (WL), and were tested at baseline, post-treatment and six-month and one-year follow-ups. All participants presented significant psychological improvement no matter if they received the HAES intervention or not. However, even if during the intervention, the three groups showed improvements, during the follow up, the HAES group continued to improve while the other groups did not, even sometimes experiencing some deterioration. Furthermore, in the HAES group only, participant's weight maintenance 12 months after the intervention was related to their psychological improvement (quality of life, body dissatisfaction, and binge eating) during the intervention. Thus, even if, in the short-term, our study did not show distinctive effects of the HAES intervention compared to SS and WL on all variables, in the long-term, HAES group seemed to present a different trajectory as psychological variables and body weight are maintained or continue to improve, which was not the case in other groups. These differential long-term effects still need to be documented and further empirically demonstrated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".