Classification de l’intensité des apprentissages interprofessionnels en contexte de stages cliniques : une proposition basée sur les fondements théoriques en formation interprofessionnelle et les résultats d’une recension narrative
Bibliographic record
Abstract
Context: In terms of interprofessional education (IPE), clinical internships are an interesting opportunity to anchor theoretical knowledge of interprofessionalism in an authentic context. However, even if there is a diversity of experiences described in the scientific literature regarding placement contexts, objectives, and modalities, no taxonomy is adapted to interprofessional internships. Purpose: This article, therefore, proposes an original classification of the intensity of experiences and learning in the context of interprofessional internship experiences based on the modality of learning offered, the degree of experience with patients, the predictability and complexity of tasks required, the focus of learning and the duration of the internship. This classification is grounded in the theoretical foundations of IPE and on data from a narrative review analysis on this subject. Method: A narrative literature review was conducted to target articles on interprofessional internship experiences in health and social services. The analysis process unfolded through iterations between data collection from the articles and analysis based on the theoretical foundations of IPE and expertise of research team members. In accordance with the instructional design research specifications, this process led to the proposal, then to the validation, of a classification of the intensity of interprofessional learning in clinical placements. Results: This classification is composed of four levels suggesting an evolution of the intensity of the educational experience according to the modality and focus of learning, the degree of exposure to patients; the predictability and complexity of the tasks and the duration of the internship. Conclusion: This type of classification will help future instigators of internships to plan, rigorously, and coherently, the progression of learners' knowledge and skills development in IPE based on their program context and goals.
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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 | 0.020 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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 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".