An investigation of the measurement properties of the physiotherapy therapeutic relationship measure in patients with musculoskeletal conditions
Bibliographic record
Abstract
The therapeutic relationship between a patient and physiotherapist has been associated with improved physiotherapy outcomes. However, there is no agreed upon measure of therapeutic relationship in physiotherapy. This paper describes a validation study of a new patient-reported measure, the Physiotherapy Therapeutic Relationship Measure (P-TREM). In this multi-site validation study, participants with musculoskeletal conditions (n = 163) completed a survey containing the P-TREM, demographic questions, a Trust in Healthcare Providers scale, and a therapeutic relationship global rating for construct validation. We investigated item quality, internal structure using exploratory factor analysis (EFA), unidimensionality, internal consistency, and construct validity. We eliminated poor performing items to optimise the length of the P-TREM. The final version of the P-TREM has 30 items. EFA suggests two domains: ‘Physiotherapist role’ and ‘Patient role’, correlation between factors was 0.71. Internal consistency was excellent. We found a low-moderate correlation between P-TREM scores and Trust in Healthcare Providers and a strong correlation between P-TREM scores and the therapeutic relationship global rating, confirming our hypotheses for convergent and concurrent validity. The P-TREM can be considered for use in clinical research to understand therapeutic relationship in the care of people with longstanding musculoskeletal conditions in outpatient, in-person settings.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".