Navigating long wait times for rheumatological health issues and suggestions for further investigations
Bibliographic record
Abstract
Canada continues to struggle with wait times for patients transitioning from primary to specialized care. Since 2019, wait times for speciality consultation and time to specialty treatment have increased significantly throughout Canada, with Ontario ranking third overall among the provinces for the longest wait times. In addition to Ontario having the largest population among the provinces, musculoskeletal (MSK) based issues make up approximately 30% of all primary care reasons for visits and ranks among the longest wait times by specialty. Thus, a large proportion of patients seeking MSK based health care in Ontario are experiencing significant delays. This commentary discusses the burden of wait times for MSK patients requiring specialized care and offers a guide on how to assess, interpret, and possibly challenge the current care model. Essentially, this commentary suggests further studies be conducted through a qualitative lens to gather and assess information about patients’ perspectives on access, feasibility, and patient-provider alignment of care. Through this subjective lens, a better understanding may be gained regarding how patients interact with the current model of care for MSK health, and this knowledge can be applied in creating patient-education resources, guiding continuing medical education, and most importantly, increasing awareness for prominent systemic impacts such as wait times.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".