Cognitive dissonance for weight stigma reduction: The development and effect of a counter‐attitudinal advocacy intervention
Bibliographic record
Abstract
Weight stigmatization is persistent and pervasive, leading to numerous negative consequences. This study developed a weight stigma reduction intervention rooted in Cognitive Dissonance Theory aimed at reducing stigma towards individuals living in larger bodies. Undergraduate students (N = 325) were randomized to one of three conditions: Cognitive Dissonance (i.e., Written Advocacy or Written + Vocalized Advocacy) or Control. Participants in both Cognitive Dissonance conditions provided a written statement advocating for a proposal benefiting individuals living in larger bodies that would be somewhat costly to participants. Those in the Written + Vocalized Advocacy condition also vocalized their arguments to further enhance dissonance. It was hypothesized that compared to Controls, participants in both Cognitive Dissonance conditions would report reduced weight stigma and greater commitment to a prosocial action, with the strongest effects for the Written + Vocalized Advocacy condition. There was a significant reduction in weight stigma across all conditions, but minimal support for significant differences between conditions. Participants in the Cognitive Dissonance conditions did not commit significantly more hours towards the prosocial action compared to Controls. There was limited support that cognitive dissonance was induced. Future studies should pre-screen participants endorsing stronger weight stigma to help ensure they are advocating for a counter-attitudinal cause.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".