Interprofessional education’s readiness among Brazilian medical students / Interprofessionelle Bildung: Bereitschaft unter brasilianischen Medizinstudierenden
Bibliographic record
Abstract
Abstract The study examines the readiness for shared learning based on interprofessional education (IPE) among Brazilian medical students participating in preceptorship programs. A total of 642 students from all six medical courses across a state in Brazil completed the Readiness for Interprofessional Learning Scale (RIPLS) and a sociodemographic questionnaire. The results, analyzed across three RIPLS factors—teamwork and collaboration, professional identity, and patient-centered care—reveal a positive inclination toward collaborative learning, though each factor was influenced by different variables. Teamwork and collaboration (factor 1) were significantly associated with gender, medical program semester, prior teamwork experience, and current clinical practice. Professional identity (factor 2) was shaped by gender, prior bachelor’s degree, type of university (public or private), and medical program semester. Patient-centered care (factor 3) showed significant relationships with gender, prior bachelor’s degree, type of university, medical program semester, and current clinical practice. These findings highlight the importance of acknowledging various demographic and educational variables when assessing student readiness for shared learning. Such insights can help medical programs refine their curricula and develop educational strategies to promote IPE, fostering collaborative healthcare practice in alignment with both national and international guidelines.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".