“The Power of Many Minds Working Together”: Qualitative Study of an Interprofessional, Service-Learning Capstone Course
Bibliographic record
Abstract
Background: An interprofessional faculty group analyzed a critical reflection assignment of students in a service-learning practicum interprofessional education (IPE) course. Students were from ten programs: physical therapy, occupational therapy, nuclear medicine technology, radiation therapy, athletic training, nursing, investigative medical science, cytotechnology, nutrition and dietetics, and clinical laboratory science. Research questions investigated what the assignments revealed about students’ application of beliefs, emotions, and behaviours, and if course objectives were met.Methods and Findings: This qualitative study retrospectively analyzed one critical reflection from the course conducted in 2011. Researchers selected a stratified sample of 40 assignments from a population of 278. Nine major themes emerged: achieving IPE outcomes, engaging in team process, learning culture/community engagement, being client/patient centred, becoming aware of behaviours, experiencing barriers, articulating beliefs, connecting with course objectives, and expressing emotions.Conclusions: In an IPE practicum course, transformative learning was evident. Students articulated beliefs, emotions, and behaviours related to interprofessional teamwork. Students expressed detailed understanding of team processes. For future research, critical reflection assignments were useful to assess student beliefs, emotions, and behaviours in a practicum course. We suggest studying practice among health professionals who have experienced IPE compared with those who have not had IPE in their professional curricula.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".