Evaluating students' perceptions of an interprofessional problem-based pilot learning project.
Bibliographic record
Abstract
Interprofessional teams provide the promise of effective, comprehensive and reliable care. Interprofessional education (IPE) promotes students' knowledge and attitudes to support interprofessional teamwork, and problem-based learning formats enable students to gain valuable teamwork experience. AIM/DESIGN: To design, implement, and evaluate an interprofessional problem-based learning module in a large Canadian university focusing on the effects of this format on students' knowledge, attitudes, and perceptions. A pre-post mixed-methods research design was used, with a convenience sample of 24 students from medicine, pharmacy, nursing, physical therapy, and occupational therapy. Participants in the module were divided into 5 teams composed of one member from each discipline. Pre-tests were delivered just prior to module participation and post-tests directly followed. Students also participated in focus groups to provide feedback about module content, process, outcomes, and practical considerations. RESULTS: Students' attitudes toward interprofessional teamwork improved from baseline to post-intervention. Mean differences were significant using paired t-tests on confidence in professional role (p <0.001), communication (p = 0.02), understanding roles of others (p = 0.002), identification with the team (p = 0.002), comfort with members (p = 0.047), cooperation with team members (p = 0.004), team perceptions (p = 0.04), decision-making (p <0.001), team efficiency (p <0.001), minimal conflict (p = 0.04), and group contributions (p = 0.03). Focus group themes indicated students were satisfied with the module, perceived increased knowledge about roles and perspectives, greater confidence to collaborate, and increased motivation to engage in intra-curricular IPE. The timing of their exposure within their respective educational programs was identified as important.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".