Outcomes and experiences of Indigenous patients in Newfoundland and Labrador’s bariatric surgery program: a pilot study
Bibliographic record
Abstract
Background: Indigenous Peoples in Canada have higher obesity rates (30%–51%) than non-Indigenous populations (12%–31%), and the Truth and Reconciliation Commission of Canada (TRC) has called for expanded health research to address disparities between Indigenous and non-Indigenous communities. We sought to compare bariatric surgery outcomes and patient experiences in Newfoundland and Labrador’s bariatric surgery program among Indigenous versus non-Indigenous patients. Methods: We conducted a mixed-methods retrospective cohort study, including patients who underwent bariatric surgery in the province’s bariatric surgery program between 2011 and 2022. We assessed metabolic outcomes through chart review and captured patient experiences with phone survey questionnaires. Results: Among the 30 included patients (8 Indigenous, 22 non-Indigenous), there were no significant differences in excess weight loss (45% v. 48%, p = 0.4), reduction in body mass index (9.5 v. 11.3, p = 0.2), comorbidity improvement (63% v. 73%, p = 0.6), or postoperative complications (25% v. 18%, p = 0.6) at 1 year. However, on a 1–10 Likert scale, Indigenous patients reported lower satisfaction with weight loss (6.3 v. 8.2, p = 0.03) and were less likely to recommend the program (5.6 v. 8.8, p = 0.04). Both groups cited similar challenges with program referral, transportation, and postoperative supports, and recommended a longer follow-up period and increased mental health counselling services. Conclusion: As a response to TRC’s Calls to Action, our study shows bariatric surgery outcomes in Newfoundland and Labrador were similar for Indigenous and non-Indigenous patients. Given their lower satisfaction with postoperative decrease in weight, Indigenous patients may benefit from being offered metabolic procedures with greater expected weight loss, such as Roux-en-Y gastric bypass and duodenal switch.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".