Management of Early- and Late-Stage Rheumatoid Arthritis: Are Physiotherapy Students' Intended Behaviours Consistent with Canadian Best Practice Guidelines?
Bibliographic record
Abstract
PURPOSE: This study examined whether physiotherapy students in a problem-based learning (PBL) curriculum intend to implement best practices for management of clients with rheumatoid arthritis (RA). METHOD: Physiotherapy students (n=49) completed a subsection of the ACREU Primary Care Survey to evaluate the concordance between intended behaviours and Canadian best practices for early- and late-stage RA, before and after completing the relevant PBL content. Changes in scores were assessed using McNemar's test for dependent proportions. RESULTS: Most students indicated that they would recommend treatments or referrals for physiotherapy/exercise, education, and occupational therapy or joint protection pre- and post-PBL (>83% and >95%, respectively). Post-PBL, more students recommended referral to a rheumatologist and disease-modifying anti-rheumatic drugs (DMARDs) for both early and late RA; however, the increase was significant only for early RA (p=0.013 and 0.031 for referral to rheumatologist and DMARDs, respectively). More students recommended psychosocial support at both stages of RA post-PBL (early RA: p<0.001; late RA: p=0.031). Although more students recommended DMARDs post-PBL, only 8 students in total made this recommendation (16%), and fewer students considered use of non-steroidal anti-inflammatory drugs. Most students (94%) did not recommend referral to a surgeon for early or late RA. CONCLUSION: Intended behaviour of physiotherapy students was more consistent with Canadian best practice guidelines for managing clients with early- and late-stage RA following the PBL curriculum. Further study is required to determine whether the students were less aware of best practices related to pharmacologic interventions and timely referral to appropriate specialists, or whether they considered these issues to be outside their scope of practice.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".