Motivated, fit, and strong – using counter-stereotypical images to reduce weight stigma internalization in women with obesity
Bibliographic record
Abstract
Background: This study aimed to use implicit retraining to change automatic associations between body size and physical activity (PA) in women with obesity to reduce weight bias internalization (WBI). Methods: A Solomon-square experimental design was used to determine the effect of a four-week online implicit retraining intervention on WBI (primary measure) and PA attitudes, self-efficacy, and self-reported behaviour (secondary measures). The intervention was a visual probe task pairing counter-stereotypical images of active individuals with obesity with positive PA-related words. In qualitative interviews, a sub-sample of participants provided feedback and recommendations for using counter-stereotypical images in PA promotion. Results: Women completed the intervention (n=48) or a control task (n=55). Results of a RM-ANOVA showed no interaction or main effect of group on WBI. A main effect of time demonstrated that both groups had reduced WBI between pre-test and post-test, through to one-week follow-up. There were no differences between groups or over time for PA attitudes, self-efficacy, or behaviour. Women who participated in an interview (n=16) discussed several benefits and drawbacks of using counter-stereotypical images. Conclusion: WBI can be reduced over a few weeks, and counter-stereotypical images may have a role.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".