Motivated, Fit, and Strong—Using Counter‐Stereotypical Images to Reduce Weight Stigma Internalisation 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 internalisation (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 telephone 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 completed interviews (n = 16) discussed several benefits and drawbacks of using counter-stereotypical images. CONCLUSION: Implicit retraining did not reduce WBI but qualitative findings support the use of counter-stereotypical PA images.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".