Teacher Training and Innovative Curriculum at the Faculty of Medical Sciences of the National University of Asunción: Strengths and Challenges of a Transformative Process
Bibliographic record
Abstract
The Faculty of Medical Sciences of the National University of Asunción undertook a significant challenge by implementing a competency-based medical curriculum through the Curricular Innovation Project and the development of the 2015 Curriculum. This initiative aimed to transform educational practices, institutional culture, and the role of teachers, supported by a comprehensive teacher training strategy and strong academic governance. This study analyses the process of teacher training and development that enabled the Faculty of Medical Sciences of the National University of Asunción to implement the competency-based curriculum. It identifies key milestones, training models, results, strengths, obstacles, and challenges, aligning them with international standards of medical education. The researchers utilized qualitative documentary analysis, employing thematic analysis with both inductive and deductive approaches. They examined institutional documentation from 2012 to 2023, including reports from the Department of Teaching Development, teaching profiles, minutes from the PIC, curricular evaluations, teacher training programs, scientific articles, conference reports, and regulatory documents. For comparative analysis, researchers incorporated international references from organizations such as the World Federation of Medical Education, the Royal College of Physicians and Surgeons of Canada competency model, and the Accreditation Council for Graduate Medical Education process. A complex, participatory curricular governance structure, led by academic commissions and the Department of Teaching Development, guided the process. Continuous professional development for teachers progressed through programs, specializations, workshops, and international consultancy, mainly focusing on authentic assessment, use of technology, curricular integration, and active methodologies. The COVID-19 pandemic accelerated digital transformation, enhancing technological literacy and pedagogical support across the Faculty. However, structural challenges persisted, including cultural resistance, the lack of a formal teaching career, care overload, and varied appropriation of the new model across departments. In summary, the article argues that the Faculty achieved national and regional distinction in competency-based medical education by establishing strong academic leadership, sustained teacher training, and effective curriculum governance. However, it emphasizes that ensuring the sustainability of this transformative process requires overcoming persistent challenges in policy, teacher career pathways, and the integration of research.
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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 | 0.033 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".