Self‐withdrawal from scheduled bariatric surgery: Qualitative study exploring patient and healthcare provider perspectives
Bibliographic record
Abstract
The objective of the study was to explore the experience of patients who self-withdrew from their scheduled bariatric surgery (BS) after completing the lengthy multidisciplinary assessment and optimization process, and to examine how these withdrawals affect healthcare providers (HCPs) in a Bariatric Centre of Excellence (BCoE). Interviews were conducted with patients who self-withdrew, within 1 month, from scheduled BS. Additionally, a focus group with HCPs from the same BCoE was completed. The data were analysed using an inductive, emergent thematic approach with open coding in NVivo 12, with comparative analysis to identify common themes between groups. Eleven patients and 14 HCPs participated. HCPs identified several behavioural and logistical red flags among patients who self-withdrew from scheduled BS. Patients and HCPs felt the decision was appropriate, owing to a patient's lack of mental preparedness for change, social supports, or fears of postoperative complications. HCPs reported frustration and described negative impacts on clinic efficiency. Additional mental health resources for patients contemplating self-withdrawal, such as peer support, were suggested. In conclusion, a patient's decision to self-withdraw from a scheduled BS is often sudden, definite, and associated with anxiety, fear of surgical risks and post-operative complications. Additional mental health resources at a BCoE may be beneficial to support patients at risk of self-withdrawal from scheduled BS.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".