How Do Trauma‐ and Violence‐Informed Care Approaches Underpin Bariatric Surgery Interventions for Type 2 Diabetes Mellitus Remission? A Scoping Review
Bibliographic record
Abstract
For individuals living with obesity, bariatric surgery is an effective intervention for type 2 diabetes mellitus (T2DM) remission. Given the established relationships between trauma and obesity, and obesity and T2DM, there is a need to examine bariatric surgical practices from a trauma- and violence-informed care (TVIC) perspective. The purpose of this scoping review was to explore and describe the extent to which the four TVIC principles-(1) understand trauma, violence, and its impact; (2) create emotionally and physically safe environments; (3) foster opportunities for choice, collaboration, and connection; and (4) use a strengths-based and capacity-building approach-have been integrated into bariatric surgery processes for T2DM remission. Following the PRISMA-ScR framework, we searched MEDLINE and EMBASE from inception to January 2024. Eligible studies included adults ≥ 18 years with T2DM undergoing bariatric surgery and reporting remission outcomes. Data were summarized narratively and charted using the TIDieR checklist. Nineteen studies were included, described in 30 publications. Despite established associations between trauma, obesity, and chronic illness, none of the included studies collected demographic data on participants' history of trauma or violence. Among included studies, mental health exclusions were common, potentially limiting access for individuals with trauma-related mental health challenges. Our findings highlight the absence of reporting TVIC principles in bariatric surgery for T2DM remission, raising concerns about emotional safety, risks for retraumatization, and long-term outcomes. Integrating the principles of TVIC throughout bariatric surgical care is essential to promote emotionally safe and inclusive care to enhance postoperative success and sustained health outcomes.
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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 | 0.027 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".