Informing equitable access to care: a cross-sectional study of travel burden to primary and rheumatology care for people with rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Achieving equity in access to care is a priority at both national and provincial levels in Canada to address health disparities. However, equitable access remains a challenge due to significantly higher rheumatoid arthritis (RA) prevalence in vast rural areas, whereas the RA care providers are primarily concentrated in the two largest cities. Rural-urban disparities in access may be partially attributed to geographic barriers. It is important to measure travel burden of people with RA for developing targeted interventions and policies to mitigate identified geographic barriers and informing equitable access to health care. METHODS: A cross-sectional study was conducted between April 1, 2019 and March 31, 2020 for people with RA in Alberta, Canada. RA cohort was identified using a validated RA case definition based on administrative health data. Travel time between patients' postal codes and providers' clinic postal codes was calculated using network analysis. Median travel time was reported at geographic area level. Wilcoxon Rank Sum Test was applied to test the statistical significance between rural-urban categories. The distance decay effect of travel time on health care utilizaton was modelled using a reverse cumulative probability approach. RESULTS: RA patients took a median of 13 min (IQR: 5-28) to visit general practitioners (GPs) and 34 min (IQR: 21-51) to visit rheumatologists. There were significant rural-urban disparities in access to GP and rheumatology care. The results showed a 4-fold difference in GP travel time (remote areas:5 min, IQR 5-79; moderate metro:20 min, IQR 7-34) and 8.7-fold difference to rheumatologist visit (remote: 226 min, IQR 165-331; metro: 26 min, IQR 17-36) across the rural-urban continuum. Remote patients experienced the longest travel time to rheumatology care but the shortest median travel time to GP care. In remote areas, travel time showed the weakest impact on health care utilization compared to other rural-urban continuum. CONCLUSIONS: Measuring the travel burden for people with RA to access care reveals patterns about the differences in how far patients travelled to seek RA care based on their residential geographic location. These findings will provide evidence to inform health care planning and address observed disparities towards the goal of achieving equitable care.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".