How Do Students Enact Group Reasoning Within Online Interprofessional Education?
Bibliographic record
Abstract
Background: The capability of an interprofessional healthcare team to reach a shared understanding through group reasoning is critical to good healthcare delivery. Models for clinical reasoning have proved useful but remain focused on individual cognitive processes. Whilst interprofessional education has steadily gained real-world traction, it is unclear how interprofessional student groups practice group reasoning when performing online tasks. Method: We analyzed the group reasoning processes with two teams of health professional students in an online interprofessional education task (n = 13). Two simulated interprofessional team meetings about a palliative case were audio recorded, transcribed, and deductively analyzed to determine the mechanisms of team deliberation using a previously published study of group reasoning.Results: The reasoning mechanisms outlined in a previous study (informationaccumulating, sense-making, and decision-making) were evident in an analysis of student group reasoning. In particular, students focused on sharing and agreeing on information, and to a lesser extent, recording information. Conclusion: Attention to the mechanisms of action may be useful to facilitate teaching interprofessional reasoning. Group reasoning may benefit from focusing student attention on these stages: 1) prioritizing and sequencing of options, methods for exposing agreement about shared information, shared understanding of the situation, and options; 2) techniques for critically evaluating information so that opportunities arise to identify when information may disrupt existing understandings; and 3) development of documentation tools to assist recording of the process.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".