Design and Delivery of the Clinical Integrative Puzzle as a Collaborative Learning Tool
Bibliographic record
Abstract
Well-designed collaborative learning tools can provide an opportunity for engaging student experiences that foster deep learning and act as a scaffold for enculturation to the profession through refinement of professional collaborative skills. The clinical integrative puzzle is a paper-and-pencil or computer-based teaching and learning activity that combines disciplinary knowledge with clinical reasoning and problem solving. Effective design and implementation of clinical integrative puzzles requires a multidisciplinary approach to design, a positive classroom climate, and a set of illness scripts (e.g., clinical cases or scenarios) that are similar yet have key differentiating features that provide students with the opportunity to exercise clinical reasoning skills. The tool allows students to co-construct knowledge and develop professional competencies and allows instructors to assess and respond to student learning in a safe and supportive environment, even with large student numbers. The tool can also be used in a summative fashion. This article provides a brief review of the use of this instructional tool and offers tips for design and implementation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".