A Feasibility Study for Utilizing a Peer-teaching Experiential Learning Activity to Alter Student Perceptions of Attributes Present in Effective Clinical Instructors
Bibliographic record
Abstract
Purpose: Many physical therapists are requested to assume the role of a clinical instructor (CI) after only one year of clinical practice. The purposes of this study are to assess the feasibility of an activity that introduces students to the responsibilities of a CI and to determine if this activity had any impact on student perceptions of attributes that are present in an effective CI. Methods: Second year DPT students enrolled in a course that utilizes case-based learning and peer-teaching activities participated in this study. Participants completed the McGill University Clinical Tutor Evaluation survey both pre- and post-learning activity. Survey results were analyzed for mean composite scores, changes in survey items ranking, and statistically significant differences in survey item responses both pre- and post- activity. Results: Mean composite scores for the McGill survey as well as mean ranks for each of the 25 survey items were identified both pre and post activity. Statistically significant differences were found by comparing student responses from surveys taken pre- and post-activity. Conclusion: The results of this study suggest that a peer-teaching experiential learning activity was feasible for influencing student perceptions of important attributes of an effective CI. These findings indicate that incorporation of similar activities into DPT entry-level curricula may aid in educating students about the responsibilities of a CI.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".