Virtual interprofessional (VIP) education, a family medicine-occupational therapy-physiotherapy collaborative experience: the perspectives of patients, learners and providers on the opportunities and challenges
Bibliographic record
Abstract
This study examined the experiences of patients, Occupational Therapy (OT), Physiotherapy (PT) and Medicine learners, Providers, and Faculty, in implementing a Virtual Interprofessional (VIP) education initiative in two academic Family Medicine (FM) collaborative clinics. A qualitative descriptive study drew on a strength-based approach as part of the evaluation of the interfaculty VIP initiative. Participants involved in VIP care were conveniently sampled. Interviews were conducted with four patients, and focus groups were held with a total of 16 providers, preceptors and learners in OT, PT and FM. Data were analyzed using content analysis and managed using NVivo12. Four main categories emerged: 1) Challenges in implementing VIP care in FM; 2) Operational challenges, 3) Facilitators of VIP care in FM; and 4) Experiential learning outcomes and benefits of VIP care. This innovation supported knowledge and insights on interprofessional competencies acquired during practice, provided inclusive and comprehensive access to care for patients, and identified opportunities to enhance medical, OT and PT education in VIP care in FM. A collaborative approach with faculty from different disciplines (FM, School of Health Professions: OT and PT) can provide ongoing opportunities for VIP care for patients, and foster IP learning and acquisition of competencies for FM, OT and PT learners and providers.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".