What can Designing Learning-by-Concordance Clinical Reasoning Cases Teach Us about Instruction in the Health Sciences?
Bibliographic record
Abstract
Introduction: Learning-by-concordance (LbC) is an online learning strategy to practice reasoning skills in clinical situations. Writing LbC clinical cases, comprising an initial hypothesis and supplementary data, differs from typical instructional design. We sought to gain a deeper understanding from experienced LbC designers to better support clinician educators' broader uptake of LbC. Methods: A dialogic action research approach was selected because it yields triangulated data from a heterogeneous group. We conducted three 90-minute dialogue-group sessions with eight clinical educators. Discussions focused on the challenges and pitfalls of each LbC design stage described in the literature. Recordings were transcribed and analyzed thematically. Results: We identified three themes by thematic analysis about the challenges inherent in designing LbC that are unique for this type of learning strategy: 1) the distinction between pedagogical intent and learning outcome; 2) the contextual cues used to challenge students and advance their learning and 3) the integration of experiential with formalized knowledge for cognitive apprenticeship. Discussion: A clinical situation can be experienced and conceptualized in many ways, and multiple responses are appropriate. LbC designers use contextual cues from their experience and combine them with formalized knowledge and protocols to write effective LbC clinical reasoning cases. LbC focuses learners' attention on decision-making in grey areas that characterize the nature of professional clinical work. This in-depth study on LbC design, indicating the integration of experiential knowledge, might call for new thinking about instructional design.
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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 | 0.006 | 0.212 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".