The Path to Translating Focus of Attention Research Into Canadian Physiotherapy, Part 3: Designing a Workshop Through Consultation With Physiotherapists and Focus of Attention Researchers
Bibliographic record
Abstract
Although researchers have consistently demonstrated the potential benefit of an external focus of attention for rehabilitation, research has shown that this finding has yet to be translated into Canadian physiotherapy. Further, specific barriers to external focus use have been reported by Canadian physiotherapists, and as a solution toward increasing physiotherapists’ use of external focus, these same physiotherapists recommended the development of an educational workshop on focus of attention. Considering this, described herein is the process of developing such a workshop, which involved (a) gathering input from physiotherapists concerning content and format via one-on-one interviews and (b) engaging in discussion about content with focus of attention researchers. Analysis of the interview data featured key content for the workshop, the types of activities to include, and a recommended sequencing for the activities: specifically, sharing didactic information on focus of attention research, then providing instruction and demonstration of external focus use, and finally, finishing with opportunities for generating and delivering external focus statements. This input, along with that of the researchers, led to the development of a two-component focus of attention workshop, which includes an asynchronous component, featuring seven self-directed learning modules and a synchronous component, which consists of a virtual group session.
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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 | 0.211 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".