Traveling Towards Timeliness: The Association Between Travel Time and Wait Time for Rheumatoid Arthritis Care
Bibliographic record
Abstract
Objectives: The aim was to measure wait times for rheumatologist consultation and disease-modifying antirheumatic drug (DMARD) treatment and examine their association with travel time to primary care practitioners (PCP) and rheumatologists within a centralized intake system, respectively. Methods: Within a centralized intake system serving 4.2 million people, we measured wait time for rheumatologist consultations and DMARD treatment for an RA incidence cohort between 1 April 2015 and 31 March 2020. Wait times were reported as the median with the interquartile range (IQR). Using multivariate logistics regression models, we examined the impact of travel times to primary/rheumatology care on wait times for rheumatologist consultation (28-day benchmark) and DMARD treatment (14-day benchmark). Travel times were defined according to quantiles and pre-defined categories. Results: The median wait time was 47 days (IQR: 18–114) for rheumatologist consultations (36% meeting the benchmark) and 35 days (IQR: 1–132) for DMARD treatment (43% meeting the benchmark). Patients living >120 min away had lower odds of meeting the 28-day consultation benchmark compared with those within 30 min (OR 0.64; 95% CI: 0.42–0.97). Compared with patients driving ≤30 min, lower odds of meeting the 14-day benchmark for DMARD treatment were observed for those driving over 60 min to PCPs (OR 0.62; 95% CI: 0.39–0.99) and patients driving 30–60 min to rheumatologists (OR 0.68; 95% CI: 0.55–0.85). Conclusion: RA management was suboptimal due to low rates of meeting RA consultation and treatment benchmarks, which was significantly associated with long travel times to both primary and RA care within a centralized triage system. This highlights the need for complementary strategies (e.g., tele-rheumatology, travel support, or alternate care providers) to ensure timely RA care in rural and remote communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".