Bibliographic record
Abstract
Interprofessional education (IPE) has been promoted as a method to enhance the ability of health professionals to learn to work together. This article examines several approaches to learning that can help IPE fulfill its expectations. The first is aimed at the transfer of learning novel situations and involves two ideas. Students need to be challenged with progressively more complex tasks and those tasks need to reflect the reality in which they will be working. Second, the learning situation needs to be structured using the five elements of best-practice cooperative learning: positive interdependence, face-to-face promotive interaction, individual accountability, interpersonal and small-group skills, and group processing. Finally, the learning process itself needs to be approached from an experiential learning framework cycling through the four-stage model of planning, doing, observing and reflecting. By using increasingly complex and relevant cases in cooperative groups with an experiential learning process interprofessional education can be successful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".