Évaluation des compétences en interprofessionnalité des étudiants en santé à l’université Grenoble Alpes
Bibliographic record
Abstract
Context: Collaboration between health care professionals is evolving to integrate the complexity of treating patients and the need for coordination to deliver fluid care pathways. The transition from multi-professionality to inter-professionality implies learning specific skills in order to optimise teamwork and collaboration. The development of interprofessional training from the beginning of medical training is essential to change mentalities and values, and to prepare the new generation of caregivers for this innovative practice. Objective: To evaluate the interprofessional skills of Grenoble health students in order to contribute to the development of interprofessional education in initial health training courses. Method: The assessment of interprofessional skills is based on a self-assessment tool (Interprofessional Collaborative Competencies Attainment Survey: ICCAS) developed in Canada and widely used in many international studies. This digital survey was distributed to UGA’s students in physiotherapy, pharmacy, medicine, maieutics, nursing and medical electroradiology. Results: A total of 290 complete responses were obtained from students in the following fields: medicine (n=93), physiotherapy (n=72), pharmacy (n=49), nursing (n=62), medical electroradiology (n=6) and maieutics (n=8). Clarification of roles and competences, expression of ideas and management of conflicts within the interprofessional team were the items for which there were differences in scores between fields. Medical students scored lower than students in the other fields on these items. Conclusion: Assessing the interprofessional skills of health students is an essential prerequisite for developing and evaluating the impact of interprofessional education at the UGA. Efforts must be continued by the teaching teams to optimise the content and methods of teaching and to enable students to acquire and consolidate their knowledge and their ability to work in interprofessional teams.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".