Greater body appreciation moderates the association between maladaptive attentional biases and body dissatisfaction in undergraduate women
Bibliographic record
Abstract
Attentional biases for weight-related information are thought to contribute to maintenance of body dissatisfaction and eating disorders. Women with greater body appreciation may pay less attention to thin-ideal cues if body appreciation protects them from negative effects of thin-ideal media, and if so, they may be less susceptible to development of maladaptive attentional biases. The present study used eye-gaze tracking to measure attention to weight-related words/images in 167 body-dissatisfied undergraduate women (aged 17-39 years) to examine the associations among body dissatisfaction, body appreciation, and attentional biases. Participants viewed displays of thin-related, fat-related, and neutral words/images while their eye fixations were tracked over 8-s intervals. We hypothesized body appreciation (as measured by the Body Appreciation Scale) would moderate the documented association between body dissatisfaction and attentional biases for thin-related information only, such that as body appreciation increased, the strength of the relationship between body dissatisfaction and attentional biases would decrease. Results indicated that body appreciation moderated the association between body dissatisfaction and attentional biases for thin-related words only. With low body appreciation, body dissatisfaction was positively associated with attention to thin-related words. With high body appreciation, there was an inverse association between body dissatisfaction and attention to thin-related words. Results suggest that body appreciation may be an effective prevention target for reducing maladaptive attentional biases.
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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 | 0.000 | 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 | 0.000 |
| 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".