Diabetes-related foot complications and amputations in a Manitoba First Nation community : a systems approach to prevention
Bibliographic record
Abstract
The prevalence of lower-extremity amputation, one of the most serious and costly complications of diabetes, is considerably higher among Manitoba First Nations relative to the general population.Considerable variation in prevalence of diabetes exists between Manitoba's First Nation communities.A case study of diabetes foot careinvolving in-depth interviews with multiple stakeholders of diabetes care was conducted in one First Nation community in Manitoba in a region with one of the highest rates of amputation in the province.Major factors contributing to poor foot health outcomes included provider practice, participation of people, coordination of care, availability of health services, access to care, funding structures, policy and jurisdiction.Significant structural factors underlying the problem of foot complications and amputation included: the lack of provision of regular foot examinations, lack of timely referral to specialist physicians, crisis work conditions, and restrictive footwear policy.The conclusions of the study support a foot care strategy that incorporates both systems change, toward improved integration and coordination of foot care systems, and policy change, toward more equitable allocation of health resources.The information gathered in this study serves as a tool for one First Nation community's govemance of foot care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".