Physiotherapy Internship: An Alternative Collaborative Learning Model
Bibliographic record
Abstract
Purpose: The purposes of this article are to (1) present a descriptive qualitative field report of a collaborative learning internship of four students assigned to two clinical instructors (CIs) (4:2 ratio) in the area of cardiorespiratory physiotherapy practice and (2) provide recommendations to CIs and universities based on this field study. Summary of Key Points: From the perspective of the students, the main advantages of this collaborative internship included increased peer support, knowledge sharing, problem solving, and a comfortable learning environment. The CIs found advantages to include more instructor peer support, increased student collaboration with assessment and treatment planning, and more opportunity for group discussion. The main disadvantages from the perspectives of both the students and the CIs were accommodating various learning styles and personalities and having less 1:1 time with the CIs. Conclusion: This collaborative learning model did not impede the quality of student education or patient care and was shown to be an educational experience appropriate for implementation in student internships. Given the positive outcomes and the recommendations provided, this 4:2 collaborative learning internship could be implemented to help meet the growing demands of increased enrolment and “hard-to-find” internships and to foster stronger supervisory skills for clinicians.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".