Exploring an IPE faculty development program using the 3-P model
Bibliographic record
Abstract
IntroductionWhile interprofessional education (IPE) activities have expanded across clinical contextsand countries in the past decade, our empirical understanding of this form of education islimited by an over-reliance upon studies which continue to focus on short term learner-focused outcomes. As a result we have only a partial understanding of the attributes neededto become an effective interprofessional facilitator.Biggs (1993) argues that when evaluating programs, a singular focus on one sub-system ofeducation (e.g., learner, facilitator, teaching context) is overly simplistic and ignores keyelements in the learning process. Systems-based approaches provide a better understandingabout how such factors, for example, presage (e.g., learner demographics, facilitatorexpertise, political climate) may affect the delivery of a program, which in turn may impactits outcomes.In this paper we describe the design of both a faculty development program created tosupport IPE facilitators and a longitudinal systems-based evaluation. Emerging findingsfrom the quantitative data set are presented and discussed.BackgroundThe purpose of this IPE faculty development program was to enlarge the cohort offacilitators at a Canadian university and affiliated teaching hospitals who could then go on toeffectively design and deliver IPE initiatives. The program adopted a blended learning
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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 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".