Understanding Physiotherapists’ Intention to Counsel Clients with Chronic Pain on Exercise: A Focus on Psychosocial Factors
Bibliographic record
Abstract
Purpose: Twenty percent of Canadians experience chronic pain. Exercise is an effective management strategy, yet participation levels are low. Physiotherapists can be key to counselling clients to engage in long-term unsupervised exercise. Yet, investigations that identify psychosocial factors related to physiotherapists’ intention to counsel are lacking. The purpose of this study was to examine whether physiotherapists’ knowledge of chronic pain, beliefs about pain, and self-efficacy to counsel on exercise predicted their intention to counsel clients with chronic pain on exercise. Method: Practicing physiotherapists ( N = 64) completed an online survey that assessed their knowledge of chronic pain, beliefs about pain, self-efficacy, and intention to counsel. A two-step hierarchical multiple regression predicted intention. Step 1 controlled for years of practice, and Step 2 included study variables significantly correlated with intention. Results: Beliefs about pain ( r = –0.35, p < 0.01) and self-efficacy ( r = 0.69, p < 0.01) were significantly correlated with intention. The overall regression model was significant, F3,60 = 18.73; p < 0.001. Self-efficacy was the sole significant predictor, t60 = 5.71, p < 0.0001, sr 2 = 28%. Conclusions: Self-efficacy may facilitate physiotherapists’ intention to counsel on exercise for chronic pain. If shown to be a causal factor, interventions that target a change in physiotherapists’ self-efficacy should be pursued.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".