Pre-licensure medical students’ knowledge and views on interprofessional learning: A qualitative concept analysis based on real-world data
Bibliographic record
Abstract
Background The several definitions of Interprofessional Education (IPE) allow for different interpretations and interchangeable terms. This study aims to determine the characteristics and attributes of the definition of IPE. Materials and methods In November 2019, 31 medical students (64.5% female) from a single institution took part in nine semi-structured interviews. We created a deductive three-level code system followed by an inductive code system based on several known IPE definitions. We extracted the main entities of the concept of IPE according to both code systems to create a framework. We used MaxQDA software for qualitative analysis. Verification of codes and categories was attained through sequential peer-debrief. Results Participants correctly named the WHO’s definition of IPE, and outlined its four main dimensions according to the Interprofessional Education Collaborative report. We found new IPE attributes and demonstrated the weight of communication and role recognition. Two-thirds of medical students mentioned IPE activities that we classified as a contrary model (without collaboration or patient-centeredness) and a minimum weight was given to the importance of patient-centeredness. Conclusion Medical students’ understanding of the concept of IPE is coherent. We deepened the understanding of previously identified definitions of IPE, and we identified new attributes of the concept. Finally, we added “well-being” as a component of interprofessionality. Clinical Trial Registration [ https://www.isrctn.com/ ], identifier [ISRCTN41715934].
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".