Current Trends in Developing Medical Students' Critical Thinking Abilities
Bibliographic record
Abstract
Health care is fallible and prone to diagnostic and management errors. The major categories of diagnostic errors include: (1) no-fault errors--the disease is present but not detected; (2) system errors--a diagnosis is delayed or missed because of the imperfection in the health care system; and (3) cognitive errors--a misdiagnosis from faulty data collection or interpretation, flawed reasoning, or incomplete knowledge. Approximately one third of patient problems are mismanaged because of diagnostic errors. Part of the solution lies in improving the diagnostic skills and critical thinking abilities of physicians as they progress through medical school and residency training. However, this task is challenging since both medical problem-solving and the learning environments are complex and not easily understood. There are many interacting variables including the motivation of the medical student (e.g. deep versus surface learning), the acquisition and evolution of declarative and conditional knowledge (e.g. reduced, dispersed, elaborated, scheme, and scripted), problem-solving strategies (e.g. procedural knowledge-guessing, hypothetical deductive, scheme inductive, and pattern recognition), curricular models (e.g. apprenticeship, discipline-based, body system-based, case-based, clinical presentation-based), teaching strategies (e.g. teaching general to specific or specific to general), the presented learning opportunities (PBL versus scheme inductive PBL), and the nature of the learning environment (e.g. modeling critical thinking and expert problem-solving). This paper elaborates on how novices differ from experts and how novices can be educated in a manner that enhances their level of expertise and diagnostic abilities as they progress through several years of medical training.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".