Tutor Experiences with Facilitating Interprofessional Problem-Based Learning
Bibliographic record
Abstract
Background: This article describes tutors’ experiences with facilitating interprofessional problem-based learning (iPBL), a topic rarely discussed in the literature. We examined tutors’ perceptions of what it was like to tutor iPBL, including the rewarding and challenging aspects. We also reported differences between new and experienced tutors.Methods and Findings: The data presented in this article were collected using three versions of a paper-and-pencil survey (N = 77, N = 99, and N = 97 for each version of the survey, respectively) and six focus groups. Surveys were completed at the conclusion of iPBL modules. Both quantitative and qualitative results indicated that tutors found the experience of facilitating iPBL to be rewarding and encountered few challenges. Tutors felt the training they received prepared them well to tutor. They also felt that facilitating iPBL increased their knowledge in the topic area of the iPBL module and of other professional roles, that it enhanced their skills as facilitators, and that they enjoyed observing students learn. New tutors reported significantly more learning and skill development than experienced tutors.Conclusions: Four lessons were derived from our research: 1) use iPBL to offer IPE; 2) invest in tutor training and support; 3) help tutors trust the process; and 4) consider tutor recruitment and retention strategies.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".