Harnessing Complexity Science for Interprofessional Education Development: A Case Study
Bibliographic record
Abstract
Background: Developing learning activities for interprofessional education (IPE) with a group of stakeholders often involves negotiation, collectivity, creativity, innovation, and unpredictable results. Theoretical approaches that can explain and support such emergent processes are needed. This case study explored the applicability of complexity science to explain the experiences of committee members as they developed learning experiences for an IPE placement in a non-acute care hospital.Methods and Findings: Data from a focus group with project steering committee members were re-analyzed through the lens of complexity science—specifically, three key principles of complex systems and five conditions for nurturing collective learning. Quotes were compared against each of these principles and conditions and, if there was a sufficient match, categorized accordingly into themes. These general themes were then sorted into clusters of sub-themes.Conclusions: Complexity science provides a useful framework for understanding the open-ended, unpredictable, and innovative IPE development process analyzed in this article. It also offers helpful practical guidelines for future learning activity and curriculum development.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".