Leaders' Views on the Implications of Digital Technologies for the Future of Physical Therapy
Bibliographic record
Abstract
Purpose: This study explores Canadian physical therapy (PT) leaders’ knowledge of digital technologies and examines their beliefs about the ways in which emerging technologies will shape PT practice in the future. Method: We recruited 11 physical therapists with leadership roles at universities, commercial technology vendors, clinics, and regulatory bodies. We conducted semi-structured interviews and then analyzed them using the DEPICT model. Results: Participants were most knowledgeable in the use of telerehabilitation in PT practice. Participants believed there were opportunities for and challenges with the further integration of technology into the profession. Participants believed technology is increasingly pressuring the profession to change. A subset of participants believed the idea of professional evolution and maturity should serve as a lens through which the profession views the implications and further integration of technology into physical therapy care. Last, we found that access to care was a focal point for discussion of the integration of technology into the profession. Conclusions: The findings underscore the importance of further research and policy development regarding the maturity of the PT profession, with a particular focus on the interconnected issues of digital technology and access to care.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".