Positive clinical placements: perspectives of students and clinical educators in rehabilitation medicine
Bibliographic record
Abstract
Background: Clinical education is a critical component of health science training programmes; however, clinical placement offers are often difficult to obtain. Although most placements are successful, a negative experience can have a significant impact on students, clinicians and clinical sites and may result in an agency's reluctance to supervise students. This study identified elements in rehabilitation medicine student clinical placements that facilitate a positive placement outcome. Methods: 152 first year rehabilitation medicine (Occupational and Physical Therapy & Speech-Language Pathology) students and 120 of their clinical educators completed a survey after their first (4-12 week) clinical placement. Students and educators rated items in 10 sections addressing demographics, general perceptions regarding a positive placement experience, physical space and resources, orientation to site, clinical educator-student relationship, relationship with the interprofessional team, location, area of practice, and financial and social supports. Findings: Ninety-eight percent of students and 97% of clinicians stated that the placement experience had been positive. Qualitative findings highlight student and clinical educator attitudes towards teaching and learning, and the team at the placement site as key factors contributing to a positive outcome. Conclusion: Despite external pressures and a lack of physical space to accommodate students, placement experiences were overwhelmingly positive. The factors contributing to this positive outcome can assist sites and university programmes in planning for future experiences.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".