Effects of physical‐activity‐related anti‐weight stigma materials on implicit and explicit evaluations
Bibliographic record
Abstract
Abstract Objective Although there exist videos and images created by Obesity Canada and similar organizations (e.g., the Rudd Center for Food Policy and Obesity), it is not known if the materials have the desired effect of reducing stigma against people with obesity and might have the opposite effect of increasing stigma. Therefore, two studies used implicit and explicit evaluations to examine the effectiveness of images and videos intended to reduce weight stigma. Methods Study 1 participants (N = 284; Mage = 31.47 years [SD = 11.26]; 177 self‐identified as women; 83 self‐identified as living with obesity) completed two implicit measures (one with images of people living with obesity and the other with control images) followed by a weight stigma questionnaire. Study 2 participants (N = 308; Mage = 31.54 years [SD = 11.35]; 153 self‐identified as women; 59 self‐identified as living with obesity) were randomly assigned to view an obesity and exercise video and images of persons with obesity, control video and images of persons with obesity, obesity and exercise video and control images, or control video and control images, followed by the implicit measures and explicit evaluation questionnaire. Results Implicit evaluations of the control images were more positive than the images of persons with obesity. Participants with no history of obesity who saw the control video and control images had lower weight stigma compared to participants in the other conditions. Conclusions Materials created to reduce weight stigma might not be effective among people with no history of obesity themselves or via a family member or friend. Intervention and health promotion researchers may wish to investigate effects of the images in combination with other messages because simply using the nonstigmatizing images is likely not enough.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".