Education Needs of Canadian Physiotherapists Working With People Living With Dementia: An Online Survey
Bibliographic record
Abstract
Purpose: To evaluate the education needs of Canadian physiotherapists in working with people living with dementia. Method: An online survey (English and French) was completed by physiotherapists registered to practice in Canada. Data collection included demographics, training in dementia, Confidence in Dementia Scale (CiD), Dementia Knowledge Assessment Scale (DKAS), Impact of Cognitive and Behavioural Symptoms on Physiotherapy Treatment, and strategies for cognitive and behavioural symptoms. A descriptive summary and analysis of outcomes based on education were performed. Results: One hundred thirty physiotherapists participated (age = 39.8 (10.7) years and 12.5 (11.0) years of practice). Education on dementia was reported by 55% during entry-to-practice and 65% after graduation. Training was reported as sufficient for 60.3% in mild, 49.6% in moderate, and 29.2% for severe dementia. The score on the DKAS was 60% and the CiD score was 67.4%. Therapists reported strategies to manage the behavioural symptoms of anxiety (67%) and agitation (61%), and cognitive symptoms of memory (79%) and language impairment (50%). Overall, 60.1% reported good job satisfaction in caring for PLWD. Conclusions: Confidence, knowledge, and job satisfaction was fair. Education needs included dementia knowledge and strategies for behavioural and cognitive symptoms. Targeted education needs to begin in entry-to-practice training and be available in post-professional courses.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".