Aprendizaje autodirigido como estrategia de educación continua en educación medica
Bibliographic record
Abstract
Self-directed learning is a potential method to promote continuated learning in Medical Education, a field flooded with scientific and technological advances. In fact, the agencies responsible for the accreditation of health education programs in the United States and Canada include it in the development of professional competencies. This has generated a growing interest and research studies in the area. Malcom Knowles established the essential components of this learning model, defined the role of the Educator, and places the student in the center, that is, the learner is who identifies his learning needs and proposes his objectives, resources and evaluation of the process. Although the cycle may have as a starting point the resolution of a problem, as in Problem-based Learning, its main difference lies in who establishes the needs and learning objectives and the role of the educator. Despite the growing interest in this learning model and the positive results related to the student's personal satisfaction and the acquisition of skills, there is not enough evidence to demonstrate its effectiveness at a national or international level, which is why this type of writing stimulates not only their knowledge, but also the realization of studies that evaluate their effectiveness, their implications, and identify the characteristics in the population that allow a better applicability.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".