Aplicación del aprendizaje basado en problemas en estudiantes de medicina de la asignatura medicina interna I de la Universidad Nacional de Colombia Sede Bogotá
Bibliographic record
Abstract
El aprendizaje basado en problemas (ABP) es un método en pedagogía que se ha venido aplicando en las escuelas de medicina del mundo durante las últimas 4 décadas. Iniciado en Mc-Master (Canadá), en Colombia su aplicación se ha realizado en varias facultades de medicina en los últimos años, brindando una herramienta muy útil a los estudiantes, no sólo para obtener el conocimiento de una manera constructivista y holística, sino para enfrentar los problemas a los que se verán retados en su vida profesional. Se presenta el diseño de un estudio exploratorio de investigación pedagógica dirigido a demostrar que la aplicación de la metodología ABP en la asignatura Medicina Interna I facilita el aprendizaje a los estudiantes de medicina, mientras se obtienen las ventajas adicionales de la metodología, como son: el desarrollo de una comunicación efectiva, el desarrollo del sentimiento de pertenencia grupal, manejo eficiente de diferentes fuentes de información, participación pertinente para la toma de decisiones entre otros. / Abstract. Problem based learning is a teaching system applied in several medical schools around of world along the last four decades, since its introduction at Mc-Master (Canada). In Colombia, It has been adopted by different universities within the last years, working as a useful tool for students; not only to obtain knowledge in a constructive and holistic way, given that, it also provides abilities to face and solve problems they will confront as professionals. A study of learning investigation addressed to demonstrate that its application facilitates internal medicine teaching for medical students will be presented. Furthermore, we intend to show additional advantages of the methodology as: effective communication skills development, efficient information management, and pertinent decision making, among others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".