Living with Bodily Changes after Weight Loss Surgery - Women’s Experiences of Food and "Dumping"
Bibliographic record
Abstract
In this article we explore women’s experiences of “dumping” following weight loss surgery. The empirical material is based on individual interviews with 22 Norwegian women. To further analyze their experiences, we build primarily on the phenomenologist Drew Leder`s notion of the “inner body.” Additionally, Simone de Beauvoir and Merleau-Ponty’s perspectives of the lived body occupy a prime framework for shedding light on different dimensions of bodily changes. The following three core themes were identified: Experiences of illness in conjunction with eating; Learning to relate to changes in the inner body and; Feelings of losing and regaining control. In different, though interconnected ways, these themes encompass an ongoing challenge in the women’s lives after the surgery: namely their efforts to establish new eating habits while at the same time working hard to relate to their changed and changing inner body, and especially to the phenomenon of “dumping”. The results points to a dilemma: namely that the gastric bypass procedure is an operation that irreversibly alters the anatomy and physiology of a healthy stomach, whereas the individual’s eating habits cannot be situated in or reduced to a particular organ, but are endemic to the lived body and its history. This insight might be of importance in the understanding of the complexity of the changes and challenges the women go through after weight loss surgery.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".