The power of prepositions: Learning with, from and about others in the context of interprofessional education
Bibliographic record
Abstract
This paper is the first of a two-part series. It presents a research study that aimed to provide a more contextual description of the commonly applied definition of interprofessional education (IPE) offered in 2002 by the Centre for the Advancement of Interprofessional Education (CAIPE) in the UK: "when two or more professions learn with, from and about each other to improve collaboration and quality of care." The study confirmed and consolidated key characteristics of IPE by exploring the meaning of with, from and about. The words with, from and about were regarded as complex. Words describing learning with each other included active engagement, co-location and equally valued. Concepts linked to learning about included knowing about people outside their professional role and interaction. Learning from others was characterized by trust, respect and confidence in others' knowledge. Although learning about others was described as the first part of learning with, from and about, there were mixed views on whether learning with or from formed the second part of the definition. Based on this work, the second paper in this series presents a proposed taxonomy for IPE that may serve to inform emerging applications for IPE in the context of education, service delivery and policy. This research contributes to an emerging understanding of IPE that will support competency development and sound curriculum design, continuing professional development and evaluation of the impact of IPE and collaboration on health outcomes.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.013 | 0.032 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".