Learning-by-Concordance (LbC): introducing undergraduate students to the complexity and uncertainty of clinical practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: A current challenge in medical education is the steep exposure to the complexity and uncertainty of clinical practice in early clerkship. The gap between pre-clinical courses and the reality of clinical decision-making can be overwhelming for undergraduate students. The Learning-by-Concordance (LbC) approach aims to bridge this gap by embedding complexity and uncertainty by relying on real-life situations and exposure to expert reasoning processes to support learning. LbC provides three forms of support: 1) expert responses that students compare with their own, 2) expert explanations and 3) recognized scholars’ key-messages.Method: Three different LbC inspired learning tools were used by 900 undergraduate medical students in three courses: Concordance-of-Reasoning in a 1st-year hematology course; Concordance-of-Perception in a 2nd-year pulmonary physio-pathology course, and; Concordance-of-Professional-Judgment with 3rd-year clerkship students. Thematic analysis was conducted on freely volunteered qualitative comments provided by 404 students.Results: Absence of a right answer was challenging for 1st year concordance-of-reasoning group; the 2nd year visual concordance group found radiology images initially difficult and unnerving and the 3rd year concordance-of-judgment group recognized the importance of divergent expert opinion.Conclusions: Expert panel answers and explanations constitute an example of “cognitive apprenticeship” that could contribute to the development of appropriate professional reasoning processes.
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.
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 | 0.666 |
| 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) | 0.001 | 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 it