Body Image After Bariatric Surgery: A Qualitative Study
Bibliographic record
Abstract
Background: Body image typically improves following bariatric surgery. However, changes to body image are quite complex. Although patients are generally satisfied with the weight loss, they also experience negative body changes such as the development of excess skin. Methods: The current study used qualitative focus group research methods to gain an in‐depth understanding of changes in body image experienced by individuals who have undergone bariatric surgery. Four focus groups were held with a total of 15 participants. Results: Three main themes emerged from the data: (a) the impact of the external world on body image (e.g., negative feedback, positive feedback, stereotypes); (b) cognitive, emotional, and behavioral changes that occur in relation to body image (e.g., changes to body image, behavior changes in personal and social domains); and (c) coping mechanisms to mitigate the negative changes to body image (e.g., avoidance of negative body image triggers, social support, cognitive restructuring of body image). Participants reported that their body image was heavily impacted by the feedback they received from the outside world. After surgery, many changes took place to their perception of their bodies, the way the felt about their bodies, and how they behaved in relation to their bodies. Conclusions: Many positive changes occur to body image following bariatric surgery. However, negative changes also occur, most notably the development of excess skin. Patients use a variety of coping skills to mitigate the negative changes to body image. Implications of these findings for the optimal care of bariatric patients are discussed.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".