Quantifying Interprofessional Learning In Health Professional Programs: The University of Manitoba Experience
Bibliographic record
Abstract
Internationally, a growing number of interprofessional education (IPE) offices are being established within academic institutions. However, few are applying educational improvement methodologies to evaluate and improve the interprofessional (IP) learning opportunities offered. The University of Manitoba IPE Initiative was established in 2008 to facilitate the development of IP learning opportunities for pre-licensure learners. The research question for this secondary analysis was: what, if any, changes in the number and attributes of IP learning opportunities occurred in the academic year 2008–2009 compared to 2011–2012? The Points for Interprofessional Scoring (PIPES) tool was used to quantify the attributes of each IP learning opportunity. Most notably in 2012, eight (73%) of 11 IP learning opportunities achieved the highest PIPES score (> 55), compared to only four (36%) in 2009. The concept of the PIPES score is introduced as an educational improvement strategy and a potential predictor of achieving the desired educational outcome: collaborative competence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".