Perceived Access to Healthcare of Indigenous Peoples in Canada With Rheumatoid Arthritis and Their First-Degree Relatives
Bibliographic record
Abstract
Objective There are complex and interrelated factors that lead to inequitable healthcare delivery in Canada. Many of the factors that underlie these inequities for Canada’s geographically dispersed Indigenous peoples remain underexamined. Methods A cohort of 831 First Nations (FN) individuals from urban and remote communities were recruited into a longitudinal study of rheumatoid arthritis (RA) risk from 2005 to 2017. Data from each participant’s initial enrollment visit were assessed using a survey that captured concerns with healthcare access. Results We found that remote participants with RA reported poor access compared to remote first-degree relatives (FDRs;P< 0.001); this difference was not observed for urban participants with RA. We observed substantial differences based on sex; female participants perceived access to care to be more difficult than male participants in both urban and remote cohorts (P< 0.001). We also observed that male participants with RA reported poor access to care compared to male FDRs. Importantly, access to care in remote communities appeared to improve over the duration of the study (P= 0.01). In a logistic regression analysis, female sex, remote location, and older age were independent predictors of poor access to care. Predictors of poor access in participants with RA also included female sex, remote location, and older age. Conclusion FN peoples living in remote communities, particularly those with an established RA diagnosis, report more problems accessing health care. Sex-based inequities exist, with FN female individuals reporting greater difficulties in accessing appropriate health care, regardless of RA diagnosis. Addressing these sex-based inequities should be a high priority for improving healthcare delivery.
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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 | 0.001 |
| Science and technology studies | 0.004 | 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".