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 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 | 0.060 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.005 | 0.040 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".