Geographic and socioeconomic factors affecting delivery of bariatric surgery across high- and low-utilization healthcare systems
Bibliographic record
Abstract
BACKGROUND: In countries with universal health coverage, the delivery of care should be driven by need. However, other factors, such as proximity to local facilities or neighbourhood socioeconomic status, may be more important. The objective of this study was to evaluate which geographic and socioeconomic factors affect the delivery of bariatric care in Canada. METHODS: ). Geographic cluster analysis and multilevel ordinal logistic regression were used to identify high-use clusters, and to evaluate the effect of geographic and socioeconomic factors on care delivery. RESULTS: Having a bariatric facility within the same public health unit as the neighbourhood was associated with a 6·6 times higher odds of being in a bariatric high-use cluster (odds ratio (OR) 6·60, 95 per cent c.i. 1·90 to 22·88; P = 0·003). This finding was consistent across provinces after adjusting for utilization rates. Neighbourhoods with higher obesity rates were also more likely to be within high-use clusters (OR per 5 per cent increase: 2·95, 1·54 to 5·66; P = 0·001), whereas neighbourhoods closer to bariatric centres were less likely to be (OR per 50 km: 0·91, 0·82 to 1·00; P = 0·048). CONCLUSION: In this study, across provincial healthcare systems with high and low utilization, the delivery of care was driven by the presence of local facilities and neighbourhood obesity rates. Increasing distance to bariatric centres substantially influenced care delivery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".