First Nations people with diabetes in Ontario: methods for a longitudinal population-based cohort study
Bibliographic record
Abstract
Background: To improve diabetes care, First Nations leaders and others need access to population-level health data. We provide details of the collaborative methods we used to describe the prevalence and incidence of diabetes in First Nations people in Ontario and present demographic data for this population compared to the rest of the Ontario population. Methods: To identify the population of First Nations people and other people in Ontario, we created annual cohorts of the Ontario population for each year between Apr. 1, 1995, and Mar. 31, 2015. Through a partnership between First Nations and academic researchers, we linked provincial population-based health administrative data stored at ICES with the Indian Register, which identifies all Status First Nations people. Our collaborative process was guided by the First Nations principles of ownership, control, access and possession (OCAP). Results: Demographic characteristics for the 2014/15 cohort (n = 13 406 684) are presented here. The cohort includes 158 241 Status First Nations people and 13 248 443 other people living in Ontario. Using postal codes, we were able to identify virtually all (99.9%) First Nations people in Ontario as living in (n = 55 311) or outside (n =102 889) a First Nations community. First Nations people were younger and more likely to live in semiurban or rural areas than the rest of Ontario’s population. Interpretation: The collaborative methodology used in this study is applicable to many jurisdictions working with Indigenous groups who have access to similar data. The Ontario cohort defined here is being used to conduct analyses of health outcomes and use of health care services among First Nations people with diabetes in Ontario.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".