The Way You Perceive Your Body Matters: Interventions Aimed to Reduce Body Dissatisfaction
Bibliographic record
Abstract
Aims: To determine if brief interventions of cognitive dissonance, media literacy, and a mindful body scan can reduce body dissatisfaction among female undergraduate students and whether mindfulness, emotion regulation, positive and negative affect, and media literacy levels have an impact on body appreciation. Study Design: The present study used an experimental design and included three intervention groups and an active control group. Place and Duration of Study: Centre for Psychological Innovation at Mount Royal University in Calgary, Alberta, Canada from January 17th to March 6th, 2020. Methodology: 78 female participants between the ages of 17 to 49 years with a mean age of 21 years (SD = 5.16). All participants underwent a body dissatisfaction induction procedure and completed self-report questionnaires on body satisfaction (using the Body Appreciation Scale), mindfulness (using the Five Facet Mindfulness Scale), emotion regulation (using the Difficulties in Emotion Regulation), positive and negative affect (using the Positive and Negative Affect Schedule), and media literacy (using the Critically Thinking About Media Messages, Media Attitudes Questionnaire, and the Sociocultural Attitudes Towards Appearance Questionnaire). Results: Three separate multiple linear regression tests revealed that there were differences in the variables predicting body appreciation scores at Time1, F(7, 70) = 12.09, p < .001, Time 2, F(7, 70) = 14.74, p< .001, and Time3, F(7, 70) = 15.07, p < .001. A one-way ANOVA revealed that body appreciation scores increased for all four conditions after completion of the intervention, F(1, 76) = .069, p = .793 but not after the body dissatisfaction induction procedure or before the intervention. Conclusion: These findings confirm that negative affect and low levels of media literacy contribute to feelings of body dissatisfaction and that brief periods of steering the mind away from the body lead to increases in body appreciation.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".