Implementation of interprofessional learning activities in a professional practicum: The emerging role of technology
Bibliographic record
Abstract
To prepare future healthcare professionals to collaborate effectively, many universities have developed interprofessional education programs (IPE). Till date, these programs have been mostly courses or clinical simulation experiences. Few attempts have been made to pursue IPE in healthcare clinical settings. This article presents the results of a pilot project in which interprofessional learning activities (ILAs) were implemented during students' professional practicum and discusses the actual and potential use of informatics in the ILA implementation. We conducted a pilot study in four healthcare settings. Our analysis is based on focus group interviews with trainees, clinical supervisors, ILA coordinators, and education managers. Overall, ILAs led to better clarification of roles and understanding of each professional's specific expertise. Informatics was helpful for developing a common language about IPE between trainees and healthcare professionals; opportunities for future application of informatics were noted. Our results support the relevance of ILAs and the value of promoting professional exchanges between students of different professions, both in academia and in the clinical setting. Informatics appears to offer opportunities for networking among students from different professions and for team members' professional development. The use of technology facilitated communication among the participants.
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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 | 0.016 | 0.029 |
| 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 | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".