Current Practices in and Barriers to Physiotherapists’ Use of Resistance Exercise with Older Adults in Acute Care
Bibliographic record
Abstract
Purpose: The purpose of this cross-sectional study was to describe physiotherapists’ current use of resistance exercise (REx) with older adults in acute care and to identify barriers to its use with this population. Methods: We developed an online questionnaire guided by the theoretical domains framework and distributed it to physiotherapists across British Columbia. We used thematic analysis to code open-text questionnaire responses. Results: One hundred and five physiotherapists completed the questionnaire (78% female; mean age 39.9 [SD 10.3] y; mean years of experience 12.4 [SD 10.3] y). Respondents reported frequently performing functional testing (95%) and assessing muscle strength (70%) in older adults, but few often prescribed REx (34%). The greatest barriers to use of REx that respondents identified were lack of prioritization of REx among other duties and perceived poor patient motivation. Open-text data analysis revealed that respondents felt that some patients were unable to perform REx and that physiotherapists lacked a clear definition of REx and sufficient support personnel. Conclusions: Addressing treatment priorities, patient motivation, and staffing resources can support physiotherapists in increasing REx use, an important strategy for reducing the incidence of hospital-associated deconditioning among older adults in acute care settings.
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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 | 0.002 | 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 | 0.001 | 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".