Teaching Interprofessional Collaboration through Experiential Learning with Behavioural Psychology, Business, and Engineering Students
Bibliographic record
Abstract
Research has indicated that interprofessional collaboration improves client outcomes, enhances work life, optimizes costs, and allows professionals to tackle complex situations with increased knowledge and creativity. However, the inherent barriers and challenges of developing effective interprofessional teams have been documented in the literature. This research explores whether teaching interprofessional collaboration improves students’ perceptions of their own interprofessional collaborative competencies. This research provides two experiential learning projects to teach interprofessional collaboration among behavioural psychology, engineering, and business students. In Study 1, interprofessional teams were presented with complex cases, and teams created a functional assessment and developed a function-based treatment using technology developed by the engineering students. During Study 2, community stakeholders provided interprofessional teams with community-based challenges. Students worked collaboratively to analyze why the challenge existed and created innovative solutions based on behavioural economics. Significant increases in the Interprofessional Collaborative Competency Attainment Scale scores were found in both studies. Sentiment analysis results suggested that most students felt that the interprofessional collaboration project benefitted them in terms of communication, collaboration, and synergy. Findings support the effectiveness of IEP in increasing student perceptions of their interprofessional collaborative competency.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".