Persistent Inequality in Access to Rheumatology Care for Females After the COVID‐19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of biological sex on wait-times to first rheumatology appointment in a central triage system before, during and after the COVID-19 pandemic. METHODS: De-identified data of patients referred to one centralised Rheumatology referral centre between November 2019 and December 2023 were extracted from the electronic medical record. Variables collected and analysed included time from referral to first appointment, biological sex, referral period, triage urgency, age, and geographic location. RESULTS: 19,681 referrals were identified. In the pre-COVID period, there was no significant difference in wait-times by biological sex or age. After adjusting for triage level, age and geographic location, females waited significantly longer in the peri-COVID period versus males (10.2 days, 95% CI 7.1, 13.3), which persisted in the post-COVID period (7.5 days, 95% CI 4.0, 11.1). Similarly, younger patients waited longer than older patients in the peri-COVID period (4.7 fewer days per decade increase in age (95% 3.9, 5.6)). This age discrepancy persisted through the post-COVID period (2.3 days, 95% CI 1.6, 3.5). Geographic location was a significant predictor of wait-times in the post-COVID period, with those outside of Edmonton waiting longer than in Edmonton. Once the change in referral pattern from Northwest Territories was accounted for, this discrepancy ceased. CONCLUSIONS: Female and younger patients have been disproportionately impacted by wait-time increases during the COVID-19 pandemic, with minimal improvements observed during the post-COVID period. These findings should prompt further investigation into the underlying causes of these observed inequities in access to rheumatology care to identify solutions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".