Evaluating a new referral pathway from physical therapists to rheumatologists: A qualitative study
Bibliographic record
Abstract
Early referral to rheumatology of people with suspected inflammatory arthritis is associated with better outcomes. Typically, these individuals are seen by a family physician who would assess the need for rheumatology referral. However, some may first consult a physical therapist where no physician referral is required. New interprofessional referral pathways, such as direct referral from a physical therapist to a rheumatologist, could enhance early access to a rheumatologist. Our objective was to explore perceptions of clinicians and people with inflammatory arthritis regarding physical therapists referring directly to rheumatologists. We used purposive and snowball sampling to recruit participants for five focus groups: rheumatologists, family physicians, physical therapists, people with inflammatory arthritis, and a mixed group of physical therapists and people with inflammatory arthritis. Thematic analysis revealed four core themes: difficulties accessing care, reluctance of family physicians and rheumatologists toward the new pathway, interprofessional relationships (or lack thereof), and opportunities along the referral pathway. The conclusions are that care must be optimized by ensuring swift referral for those who require it; and that there is a need for knowledge translation to all actors on the advantages of this new pathway.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".