Wait Times for Physical and Occupational Therapy in the Public System for People with Arthritis in Quebec
Bibliographic record
Abstract
PURPOSE: Although arthritis is the leading cause of pain and disability in Canada, and physical therapy (PT) and occupational therapy (OT) are beneficial both for chronic osteoarthritis (OA) and for inflammatory arthritis such as rheumatoid arthritis (RA), there appear to be problems with access to such services. The aim of this study was to document wait times from referral by physician to consultation with PT or OT in the public health care system for people with arthritis in Quebec, Canada. METHOD: Appointments were requested by telephone, using hypothetical case scenarios; wait times were defined as the time between initial request and appointment date. Descriptive statistics were used to examine the wait times in relation to diagnosis, service provider and geographic area. RESULTS: For both scenarios (OA and RA) combined, 13% were offered an appointment within 6 months, 13% offered given an appointment within 6-12 months, 24% were told they would need to wait longer than 12 months, and 22% were refused services. The remaining 28% were told they would require an evaluation appointment for functional assessment before being given an appointment for therapy. No difference was found between RA and OA diagnoses. CONCLUSIONS: Our study suggests that most people with arthritis living in the province of Quebec are not receiving publicly accessible PT or OT intervention in a timely manner.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".