Physiotherapist and Patient Experiences of Team-Based Interprofessional Collaboration During the COVID-19 Pandemic in Canada
Bibliographic record
Abstract
Purpose: To examine the perspectives of physiotherapists and physiotherapy patients regarding team-based interprofessional collaboration during the COVID-19 pandemic in Canada. Method: This mixed methods study combined online surveys (physiotherapists, patients) and qualitative semi-structured interviews (patients). Descriptive statistics and thematic analysis summarized the quantitative and qualitative data before final data integration. Results: Physiotherapists ( n = 334) and patients ( n = 784) participated in the surveys, while 19 patients were interviewed. Less than half (48%) of physiotherapists reported delivering care as part of multidisciplinary teams and 38% of these individuals reported that the pandemic decreased their ability to deliver team-based, interprofessional care. Physiotherapists found that team-based care was negatively impacted by communication challenges, poor care coordination, and patients lacking access to other health professionals. While over one-third (38%) of patients reported poor care coordination between health professionals, qualitatively many patients reported that these challenges were similar pre-pandemic. They also experienced increased communication challenges and emphasized poor access to general practitioners and specialists. Both groups saw future opportunities for increased use of virtual care to improve team-based health care delivery. Conclusions: Physiotherapists and patients had varied experiences with aspects of team-based care during the pandemic that included challenges with communication, care coordination, and ability to access health professionals. Improved training and implementation of virtual care may enhance interprofessional collaboration and improve patient care in the future.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".