Exploring how students think: a new method combining think‐aloud and concept mapping protocols
Bibliographic record
Abstract
OBJECTIVES: A key element of medical competence is problem solving. Previous work has shown that doctors use inductive reasoning to progress from facts to hypotheses and deductive reasoning to move from hypotheses to the gathering of confirmatory information. No individual assessment method has been designed to quantify the use of inductive and deductive procedures within clinical reasoning. The aim of this study was to explore the feasibility and reliability of a new method which allows for the rapid identification of the style (inductive or deductive) of clinical reasoning in medical students and experts. METHODS: The study included four groups of four participants. These comprised groups of medical students in Years 3, 4 and 5 and a group of specialists in internal medicine, all at a medical school with a 6-year curriculum in France. Participants were asked to solve four clinical problems by thinking aloud. The thinking expressed aloud was immediately transcribed into concept maps by one or two 'writers' trained to distinguish inductive and deductive links. Reliability was assessed by estimating the inter-writer correlation. The calculated rate of inductive reasoning, the richness score and the rate of exhaustiveness of reasoning were compared according to the level of expertise of the individual and the type of clinical problem. RESULTS: The total number of maps drawn amounted to 32 for students in Year 4, 32 for students in Year 5, 16 for students in Year 3 and 16 for experts. A positive correlation was found between writers (R = 0.66-0.93). Richness scores and rates of exhaustiveness of reasoning did not differ according to expertise level. The rate of inductive reasoning varied as expected according to the nature of the clinical problem and was lower in experts (41% versus 67%). CONCLUSIONS: This new method showed good reliability and may be a promising tool for the assessment of medical problem-solving skills, giving teachers a means of diagnosing how their students think when they are confronted with clinical problems.
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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 | 0.001 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".