Building a theoretical model for virtual interprofessional education
Bibliographic record
Abstract
BACKGROUND: Virtual interprofessional education (IPE) has emerged as a promising alternative to traditional in-person IPE. However, theoretical frameworks to support virtual interprofessional learning are not well established. Two theoretical frameworks emerged as relevant to virtual IPE: (1) the Canadian Interprofessional Health Collaborative (CIHC) interprofessional learning framework and (2) Dornan's Experience-Based Learning Model (ExBL) of workplace learning. In this study, we sought to explore virtual IPE using both frameworks to develop new theoretical understandings and identify assumptions, gaps and barriers. METHODS: This was a qualitative study. Semi-structured interviews were conducted with medical and nursing student participants (n = 14) and facilitators (n = 3) from virtual IPE workshops. Transcripts were analysed using directed content analysis methodology, informed by the CIHC and ExBL frameworks. Themes were explored using mind-mapping transitional coding. Data collection and analysis were continued iteratively until themes with adequate conceptual depth, relevance and plausibility were identified. RESULTS: Three themes were identified: (1) a shift in the balance of personal and professional, (2) blunted sociologic fidelity and (3) uncertainty and threats to interpersonal connections. Professional distinctions and hierarchies are blurred virtually. This contributed to an increased sense of psychological safety among most learners and lowered the threshold for participation. Separation from workplace sociologic complexity facilitated communication and role clarification objectives. However, loss of immersion may limit deeper engagement. Interprofessional objectives that rely on deeper sociological fidelity, such as conflict resolution, may be threatened. Informal interactions between learners are hindered, which may threaten organic development of interprofessional relationships. CONCLUSIONS: Role clarification and communication objectives are preserved in virtual IPE. Educators should pay close attention to psychological safety and sociologic fidelity-both to leverage advantages and guard against threats to connection and transferability. Virtual IPE may be well suited as a primer to in-person activities or as scaffolding towards interprofessional workplace practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".