Understanding the factors influencing physiotherapists’ attitudes towards working with people living with dementia
Bibliographic record
Abstract
Background: Research suggests healthcare professionals feel uncomfortable or inadequately prepared to provide care to people living with dementia. Importantly, research on the attitudes of physiotherapists toward people with dementia is limited. The objective was to assess personal, educational, and clinical experiences on physiotherapists’ attitudes toward working with people with dementia.Methods: An online survey was completed by registered physiotherapists. Data were collected on their dementia knowledge, confidence, and attitudes. Structural equation modeling (SEM) evaluated the factors associated with attitudes of physiotherapists.Results: A total of 231 physiotherapists completed the survey. Participants’ scores on knowledge of dementia were excellent. Interactions with people with dementia were positive (67.4%) and access to rehabilitation was important (70.4%). However, most respondents reported a lack of confidence and strategies to successfully deal with cognitive (42.5%) or behavioral (58.3%) symptoms. In the SEM, only education (p = .048) was significantly related to attitude. Specifically, more education was related to more positive attitudes.Conclusions: Scores on knowledge of dementia were high. Yet, most respondents reported reduced confidence from a lack of skills to manage behavioral or cognitive symptoms associated with dementia. More education related to working with people with dementia was significantly related to positive attitudes among physiotherapists.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".