Linked administrative data’s role in Victoria’s first social impact investment, journey to social inclusion, working to end chronic homelessness
Bibliographic record
Abstract
IntroductionIndigenous people worldwide are overrepresented and adversely effected by diabetes and its complications. Optimal glycemic control and lipid monitoring is fundamental to the management of diabetes. This study linked population level data to assess monitoring, treatment and control of blood sugars and lipids in First Nations’ people in Ontario. Objectives and ApproachWe linked 17 Ontario population-based health administration datasets at the individual level with the Indian Register dataset. The latter provides information on all registered or Status First Nations people in Canada . Age and sex-adjusted rates of HbA1c and lipid monitoring were calculated for each 12-month period from April 1, 1995, to March 31, 2015.). We assessed the proportion of individuals with diabetes whose HbA1c and lipid values were controlled. To capture prescriptions for antidiabetic drugs, we used the Drug Identification Number database to identify all antidiabetic drugs and linked these to the Ontario Drug Benefit database to capture prescription information. ResultsCompared with other people in Ontario, First Nations people with diabetes are monitored less for key indicators of diabetes control. In 2014/15, 37.0% of First Nations people with diabetes living in First Nations communities had their blood sugar levels monitored compared to 45.0% of other people in Ontario. A similar pattern was shown for lipid level monitoring, with 48.3% of First Nations people living in First Nations communities, and 65.8% of other people in Ontario having recorded lipid measurements. Conclusion / ImplicationsEarly screening for complications and screening for hemoglobin A1c is strongly recommended.
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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 | 0.015 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".