The Path to Translating Focus of Attention Research into Canadian Physiotherapy, Part 2: Physiotherapist Interviews Reveal Impacting Factors and Barriers to Focus of Attention Use
Bibliographic record
Abstract
Although researchers have highlighted the benefits of adopting an external focus of attention for rehabilitation, studies have consistently revealed low external focus use by physiotherapists. Consequently, the purpose of this research was to explore factors influencing physiotherapists’ focus of attention use and to gain insight into the barriers, and potential solutions, related to effective external focus use. Eight physiotherapists, working with musculoskeletal rehabilitation clients, first completed the Therapists’ Perceptions of Motor Learning Principles Questionnaire and then participated in virtual one-on-one interviews. The interviews followed a semistructured interview guide and were analyzed using a total quality framework approach to qualitative content analysis. Data showed that physiotherapists’ focus of attention use was influenced by physiotherapist, client, and task characteristics/experiences, as well as focus of attention statement provision strategies. Furthermore, the main barriers discussed related to educational experiences, reinforcement of internal focus of attention statement use and aspects related to research. Solutions presented to these barriers included the incorporation of focus of attention content into both the Canadian physiotherapy curriculum and continued education. Overall, these results advance our knowledge of factors underlying physiotherapists’ focus of attention use and barriers that must be overcome to successfully translate the focus of attention research into physiotherapy.
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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 | 0.064 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".