Creating an Interprofessional Education Experience for Pre-service Education and PK-12 Schools
Bibliographic record
Abstract
As educators of pre-service school librarians, we focus on facilitating collaboration with the entire school community. Other helping professions have a similar focus, often termed interprofessional practice. Pre-service educators and other health and human resources professionals need to be taught how to work as part of an interprofessional collaborative team. The Interprofessional Education team at Longwood University developed a model for interprofessional education at Longwood University. Background information, specific steps, and tips for success are shared for institutions or PK-12 schools interested in implementing a similar 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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.007 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.033 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 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".