Experiences of Physiotherapy Students, Health Care Providers, and Patients with a Role-Emerging Student Clinical Placement in an Emergency Department: A Qualitative Study
Bibliographic record
Abstract
Purpose: To understand the experiences and perspectives of physiotherapy (PT) students, their clinical instructor, nurses, physicians, and patients with a role-emerging student clinical placement in an emergency department (ED) and to identify barriers and facilitators in implementing this placement model. Method: We conducted qualitative semi-structured interviews with 6 PT students, 1 PT clinical instructor, 15 nurses, 12 physicians, and 17 patients. Five researchers independently coded the transcribed interviews and performed thematic analysis in an interpretive description tradition with frequent peer debriefing and reflexive discussions. Results: Students and their clinical instructor reported that the placement setting provided a unique learning opportunity. Patients and ED staff noted that involving the PT students in patient care delivery improved the musculoskeletal assessments and self-management advice provided to patients. Identified barriers included students’ inability to chart in the electronic medical record, lack of bed space, and lack of clarity about students’ scope and abilities. Reported facilitators included positive perceptions of the students’ supervision and a perceived positive impact on patient care and the health care team. Conclusions: Participants reported positive experiences with the student ED placement and recommended similar placements in the future. Understanding barriers and facilitators in implementing PT student clinical placements in an ED can inform future placements.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".