A Survey of Hospital-Based Physiotherapists’ Roles and Responsibilities during the COVID-19 Pandemic in Ontario, Canada
Bibliographic record
Abstract
Purpose: The COVID-19 pandemic and resulting high number of individuals requiring hospitalization has caused health care systems worldwide to alter hospital policies and procedures. This study examined how changes in hospital operations between March 2020 and March 2021 affected physiotherapists’ roles and responsibilities in Ontario, Canada. Method: Between February and March 2021, we conducted a cross-sectional study using an online survey of physiotherapists employed in acute care and rehabilitation hospitals. Results: Among 230 respondents, 82 (35.7%) reported being redeployed at some point during the study period to new settings or areas of practice. Physiotherapists typically working in outpatient settings were the most likely to be redeployed (63.3%), with 62.9% of respondents reporting caring for COVID-19 patients. Among 37.1% of respondents reporting undertaking new responsibilities (e.g., personal support work, nursing, infection control), 72.0% reported being confident in their abilities; however, only 49.4% felt adequately trained. Conclusions: Hospital-based physiotherapists in Ontario, Canada took on a variety of traditional and non-traditional responsibilities during the first year of the pandemic. Although confident in their abilities, feelings of being inadequately trained highlight the need for improved processes when taking on new responsibilities to support delivery of patient care and physiotherapists’ well-being.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".