The impact of cognitive restructuring and self-compassion strategies on negative body image among women with higher body weight: an experimental investigation
Bibliographic record
Abstract
Individuals with higher body weight are at a greater risk of having negative body image (Friedman & Brownell, 1995). Yet current body image interventions, such as Cognitive Behavioural Therapy (CBT), are largely tested with individuals with normal weight or individuals with eating disorders. Furthermore, cognitive restructuring, one of the key components of CBT for body image (Alleva et al., 2015), relies on the assumption that negative cognitions or appraisals regarding the body are unbalanced or distorted in some way. However, people with higher body weight are 50% more likely to experience major discrimination based on their weight status and thus may possess some “evidence” from lived experience of weight bias that would lend support to their negative body-related thoughts (Puhl & Brownell, 2001; 2006). The use of compassion-focused approaches might be particularly helpful in overcoming these obstacles. Self-compassion refers to the capacity for mindfully reflecting on one’s own perceived flaws, mistakes, or wrongdoings with kindness and with an appreciation for the inherent imperfection in everyone (Neff, 2013). The present study tested the impact of various thinking strategies for managing negative body image in women with higher body weight after getting on the scale, a commonly distressing body image trigger (Ogden & Evans, 1996). Participants (N = 79) were recruited from the community and screened for moderate body dissatisfaction. They were randomly assigned to receive a single training session in cognitive restructuring (CR), self-compassion (SC), or distraction (Control) strategies after being weighed. Participants in all three of the groups reported improvements in body dissatisfaction and negative affect immediately following the training. Relative to those in the Control group, those participants who received training in CR or SC strategies reported greater improvements in body image, body image flexibility, self-compassion, and cognitive distortions one week after the training. These findings suggest that CR and SC strategies may be helpful in improving the distress associated with being weighed among women with higher body weight. The results may have broader implications for the development of psychosocial interventions focused on improving body image among these individuals.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".