Navigating the Grey Zone of Physiotherapy Assistant Autonomy in Home Care: Perspectives of Physiotherapists and Physiotherapy Assistants
Bibliographic record
Abstract
Purpose: To explore perspectives and experiences regarding the autonomy of physiotherapist assistants (PTAs) among physiotherapists and PTAs providing home care services in Ontario since the introduction of PTAs into home care rehabilitation teams. Method: For this qualitative study, we conducted semi-structured interviews with 10 physiotherapists and 5 PTAs working in home care. We analyzed interview transcripts using the DEPICT model. Results: Participants described navigating a grey zone characterized by a lack of clarity about acceptable levels of PTA autonomy. Four interrelating factors shaped the extent to which PTAs practised with autonomy: system influences (number of physiotherapy visits, professional guidelines), patient complexity (status, comorbidities), perceived PTA competence (skills, training), and the physiotherapist–PTA relationship (trust, communication). Conclusions: New practice models in home care have impacted the role of both physiotherapists and PTAs. Home care agencies should facilitate emerging professional relationships and address autonomy-related challenges, such as trust and competence, to promote high-quality client-centred 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".