The Path to Translating Focus of Attention Research Into Canadian Physiotherapy, Part 1: Physiotherapists’ Self-Reported Focus of Attention Use Via a Study-Specific Questionnaire
Bibliographic record
Abstract
The focus of attention literature has shown robust findings for the benefits of providing statements that focus on the movement effect or outcome (external focus of attention [EFOA]) as opposed to focusing on the movement kinematics (internal focus of attention). Observational studies, however, have revealed that physiotherapists use fewer EFOA statements than internal focus of attention statements in their practice. Most evidence in this regard has been from non-Canadian physiotherapists working in stroke rehabilitation; consequently, we sought to examine whether Canadian physiotherapists working with various rehabilitation populations also use EFOA statements to a lesser extent than internal focus of attention statements. The “Therapists’ Perceptions of Motor Learning Principles Questionnaire (TPMLPQ)” was thus designed and data from 121 Canadian physiotherapists showed low relative frequencies of EFOA use (31.3% ± 14%) averaged across six hypothetical scenarios. A higher EFOA was reported, however, for two of the six scenarios: a functional reaching scenario (55.5% ± 37.0%) and pelvic floor task (65.6% ±32.9%). This data suggest that the findings of EFOA benefits have not been widely translated into Canadian physiotherapy settings; furthermore, the findings of the scenario-dependency warrant future investigation into factors, such as task characteristics, that may influence physiotherapists’ FOA use.
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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 | 0.089 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".