Motivated, fit, and strong: Using non-weight stigmatizing images and positive physical activity words in an implicit retraining task to reduce internalized weight bias in women living with obesity
Bibliographic record
Abstract
Women with obesity are frequently teased, judged, and shamed due to their weight. Internalizing weight-related stereotypes (e.g., that a person with obesity is inherently lazy and unmotivated) has been associated with physical activity (PA) avoidance. The purpose of this Solomon-square (half the participants in each condition completed a pretest) four-week online study was to determine if internalized weight bias (IWB) could be reduced in women with obesity using an implicit retraining task. This was a visual probe task that repeatedly paired non-stereotypical images of individuals with obesity being active with positive PA-related words (e.g., motivated, strong). This task was hypothesized to change automatic associations between PA and obesity (i.e., counter stereotypes related to weight and PA) through principles of evaluative conditioning. Participants in the experimental group (n=49) completed the implicit retraining once per week for three weeks. Their IWB scores were compared to those of participants in the control group (n=53) who read Canada's PA guidelines and were asked to create weekly PA goals. There was no effect of completing pretest measures on post-test IWB. Results of a repeated measures Analysis of Variance showed that women in the implicit retraining group had lower IWB than those in the comparison group at post-test (d=0.46) and at one-week follow-up (d=0.43), F (1, 102) = 5.583, p=.020. These findings suggest that portraying individuals with obesity in positive, non-stereotypical ways when promoting PA could reduce the effects of IWB and may play a role in promoting PA in women with obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".