Students’ Perspectives on Their Experience in Clinical Placements: Using a Modified Delphi Methodology to Engage Physiotherapy Stakeholders in Revising the National Form
Bibliographic record
Abstract
Purpose: We developed an evidence-informed Student Evaluation of the Clinical Placement form. This form gives students the opportunity to share their feedback and perceptions of their clinical placement experiences and provides meaningful data to all stakeholders. Method: We used a modified Delphi process to engage a sample of national stakeholders: physiotherapy clinical education leads of academic departments, centre coordinators of clinical education, clinical instructors, and students. An expert consultant panel, in addition to the investigators, reviewed the responses from each round and helped develop the questionnaire for the subsequent round and finalize the evaluation form. Results: The response rate was 65.3% (47 of 72) for Round 1, 76.6% (36 of 47) for Round 2, and 100% (36 of 36) for Round 3. After three rounds of questionnaires, 89% of participants thought that the evaluation form met their needs. Conclusions: We developed a revised Student Evaluation of the Clinical Placement form that is informed by the literature and meaningful to all stakeholders. This form is being implemented in physiotherapy university programmes across Canada to enable students to share their experiences at clinical sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.113 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".