Experiência com metodologias ativas em uma disciplina de Entrevista Clínica Centrada na Pessoa
Bibliographic record
Abstract
Active methodologies are strategic for meaningful learning. In the health field, they represent an alternative to the content volume sometimes mechanically exposed to students before they can apply it in the practical field. Thus, they encourage the development of skills that will be used in practical activities with patients and other professionals. We present the experience developed in the discipline Person-Centered Clinical Interview (ECCP) at Ouro Preto Federal University for one semester, using an educational methodology based on gamification and clinical simulation through the appointment evaluation by the Calgary-Cambridge guide. Strategies, motivations, and results of an intervention are described, involving dramatization of activities and participation of students individually, in groups, and collectively in three moments, providing a significant learning experience. Adherence to the strategy was observed, expanding ECCP knowledge, upgrading learning and reflections on their respective practices during the simulations, registering the involvement of all actors in an active education-learning process.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".