Draping Education to Promote Patient Dignity: Canadian Physiotherapy Student and Instructor Perceptions
Bibliographic record
Abstract
PURPOSE: To determine the perceptions of educators and students in Canadian entry-level professional physiotherapy programmes with respect to the current draping curriculum and the methods of delivery of that content and to determine if there is a need for additional draping education time and resources in these programmes. METHODS: Canadian university physiotherapy students (n=127) and educators (n=183) completed questionnaires designed by the authors. Data were collected via Survey Monkey, exported as Excel files, and analyzed using descriptive statistics and Pearson chi-square analysis. RESULTS: Students and educators agreed that dignity as a concept and draping as a skill to protect patient dignity are both important and should be included in Canadian physiotherapy curricula. Respondents also agreed that students often have difficulty with draping. Educators identified barriers to teaching draping while students identified components of an effective educational resource on draping. CONCLUSIONS: To enhance the development of effective draping skills among entry-level physiotherapists, draping education should be included in Canadian physiotherapy curricula. An effective draping educational resource should be developed for educators and students. To enhance the development of effective draping skills among entry-level physiotherapists, draping education should be included in Canadian physiotherapy curricula. An effective draping educational resource should be developed for educators and students.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".