Interaction in online interprofessional education case discussions
Bibliographic record
Abstract
This study investigated online interaction within a curriculum unit at the University of Toronto, Canada that included an interprofessional case study discussion in a mixed-mode (face-to-face and online) format. Nine of the 81 teams that completed the four-day curriculum were selected for detailed review based on the attitudes students expressed on a survey about the value of collaborating online for enhancing their appreciation of other health care professions. Five of the teams selected were 'positive' and four were 'negative'. The responses to other survey items by members of these teams were then compared, as well as their message posting patterns and the content of their online discussions. Differences between the two sets were situated within a theoretical framework drawn from the contact theory, social interdependence theory, and the Community of Inquiry model. Institutional support in the form of facilitator involvement, individual predispositions to online and group learning, the group composition, the learning materials, task and assignment, and technical factors all affected the levels of participation online. Discourse and organizational techniques were identified that related to interactivity within the online discussions. These findings can help curriculum planners design interprofessional case studies that encourage the interactivity required for successful online discussions.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".