Collaboration behind-the-scenes: key to effective interprofessional education
Bibliographic record
Abstract
A variety of stakeholders, including students, faculty, educational institutions and the broader health care and social service communities, work behind-the-scenes to support interprofessional education initiatives. While program designers are faced with multiple challenges associated with implementing and sustaining such programs, little has been written about how program designers practice the interprofessional competencies that are expected of students. This brief report describes the backstage collaboration underpinning the Dalhousie Health Mentors Program, a large and complex pre-licensure interprofessional experience connecting student teams with community volunteer mentors who have chronic conditions to learn about interprofessional collaboration and patient/client-centered care. Based on our experiences, we suggest that just as students are required to reflect on collaborative processes, interprofessional program designers should examine the ways in which they work together and take into consideration the impact this has on the delivery of the educational experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".