A cross-sectional analysis of the relationship between diabetes and health access barriers in an urban First Nations population in Canada
Bibliographic record
Abstract
OBJECTIVE: This study explores the relationship between health access barriers and diabetes in an urban First Nations population in Canada. DESIGN: Data from a self-identified urban First Nations population were collected using respondent-driven sampling (RDS). As no clear approach for regression modelling of RDS data is available, two logistic regression modelling approaches, including survey-based logistic and generalised linear mixed models, were used to explore the relationship between diabetes and health barriers of interest, including access to healthcare, food, housing and socioeconomic factors. SETTING: Hamilton, Ontario, Canada. PARTICIPANTS: This cross-sectional study used data collected from the Our Health Counts study, in partnership with the De dwa da dehs nye>s Aboriginal Health Centre, which recruited 554 First Nations adults living in Hamilton using RDS. RESULTS: After adjusting for covariates, multivariable regression techniques showed a statistically significant relationship between a self-reported diagnosis of diabetes and a lack of culturally appropriate care among urban First Nations peoples (OR: 12.70, 95% CI 2.52 to 57.91). There was also a trend towards a relationship between diabetes and not having a doctor available in the area, feeling that healthcare provided was inadequate and a lack of available healthcare services in the area. CONCLUSIONS: Urban First Nations peoples who felt the health service they received was not culturally appropriate were more likely to have diabetes, compared with those who did not feel the service they received was culturally inappropriate. Establishing more healthcare services that integrate First Nations cultures and traditions could improve access to care and the course of treatment for urban First Nations peoples living with diabetes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| 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".