Merging Problem-Based Learning with Simulation-Based Learning in the Medical Undergraduate Curriculum: The PAIRED Framework for Enhancing Lifelong Learning
Bibliographic record
Abstract
Lifelong learning is an essential trait that is expected of every physician. The CanMeds 2005 Physician Competency Framework emphasizes lifelong learning as a key competency that physicians must achieve in becoming better physicians. However, many physicians are not competent at engaging in lifelong learning. The current medical education system is deficient in preparing medical students to develop and carry out their own lifelong learning curriculum upon graduation. Despite understanding how physicians learn at work, medical students are not trained to learn while working. Similarly, although barriers to lifelong learning are known, medical students are not adequately skilled in overcoming these barriers. Learning to learn is just as important, if not more, as acquiring the skills and knowledge required of a physician. The medical undergraduate curriculum lacks a specific learning strategy to prepare medical students in becoming an adept lifelong learner. In this article, we propose a learning strategy for lifelong learning at the undergraduate level. In developing this novel strategy, we paid particular attention to two parameters. First, this strategy should be grounded on literature describing a physician's lifelong learning process. Second, the framework for implementing this strategy must be based on existing undergraduate learning strategies to obviate the need for additional resources, learner burden, and faculty time. In this paper, we propose a Problem, Analysis, Independent Research Reporting, Experimentation Debriefing (PAIRED) framework that follows the learning process of a physician and serves to synergize the components of problem-based learning and simulation-based learning in specifically targeting the barriers to lifelong learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".