Quality of Care for First Nations and Non-First Nations People with Diabetes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Compared with non-First Nations, First Nations People with diabetes experience higher rates of kidney failure and death, which may be related to disparities in care. This study examined First Nations and non-First Nations People with diabetes for differences in quality indicators and their association with kidney failure and death. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Adults with diabetes and an outpatient creatinine in Alberta from 2005 to 2008 were identified. Logistic regression was used to determine the likelihood of process of care indicators (measurement of urine albumin/creatinine ratio [ACR], LDL, and hemoglobin A1C [A1C]) and surrogate outcome indicators (achievement of LDL and A1C targets). Cox regression was used to determine the association between lack of achievement of indicator targets and each of kidney failure and death. RESULTS: This study identified 140,709 non-First Nations and 6574 First Nations People with diabetes. There was a significant interaction between First Nations status and CKD for the outcomes (P<0.01); therefore, results are stratified by CKD. Among participants without CKD, First Nations People were less likely to receive process of care indicators and achieve target A1C compared with non-First Nations People. For those with CKD, First Nations People were as likely to receive these indicators (other than LDL) and achieve LDL and A1C targets. Lack of LDL and A1C assessment and achievement of targets were associated with increased risk of kidney failure and death similarly for both groups. CONCLUSIONS: Compared with non-First Nations, First Nations People with diabetes but without CKD experience disparities in assessment of quality indicators and achievement of A1C target.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".