Evaluation of a mobile diabetes care telemedicine clinic serving Aboriginal communities in northern British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: In British Columbia, Aboriginal diabetes prevalence, hospitalization and mortality rates are all more than twice as high as in the rest of the population. We describe and evaluate a program to improve access to diabetes care for Aboriginal people in northern communities. STUDY DESIGN: Cost-effectiveness evaluation. METHODS: A diabetes nurse educator and an ophthalmic technician travel to Aboriginal reserves, offering people with diabetes services recommended in current clinical practice guidelines: retinopathy screening by digital retinal fundus photography, glaucoma screening by tonometry, point-of-care urine and blood testing to detect microalbuminuria and dyslipidemia and to measure glycated hemoglobin, foot examinations and foot care advice, blood pressure and height and weight measurement and diabetes care advice. Via electronic communication, an ophthalmologist and an endocrinologist in Vancouver review the findings and supervise the mobile clinic staff. RESULTS: During the first year, 25 clinics were held at 22 sites, examining 339 clients with diabetes. Exit surveys showed high levels of client satisfaction. Mean cost per client (Cdn dollars 1,231) was less than for the alternative, transporting clients to care in the nearest cities (Cdn dollars 1,437). CONCLUSIONS: The mobile clinic is cost-effective and improves access to the recommended standard of diabetes 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".