Bibliographic record
Abstract
PURPOSE: To understand how experienced clinicians formulate cases and to use this understanding to explore the broader processes involved in how clinicians solve complex problems in their daily work. Case formulation is a process that allows clinicians to provide a tentative explanation for why a patient with a certain condition presents in a particular way at a particular time. METHOD: In this constructivist grounded theory study, the authors conducted semistructured interviews with 12 physicians (9 experienced clinicians, 3 new graduates and residents) from the University of Toronto Division of Developmental Pediatrics between July and December 2012. They used a constant comparative analysis to identify themes and iteratively developed a thematic structure, which one researcher applied to the entire data set. They maintained a detailed audit trail throughout the process. RESULTS: Experienced clinician participants articulated three interconnected themes that characterize their complex problem solving during case formulation: (1) interpreting individual patient factors in the context of medical and clinical knowledge, (2) strategically co-constructing the case formulation with parents and team members, and (3) refining the case formulation over time. CONCLUSIONS: Findings suggest that these interpretive, strategic, and longitudinal processes appear to be central to the complex problem solving of experienced clinicians engaged in case formulation. They illuminate how clinicians integrate multiple competencies when they solve complex problems in their daily work. Exploring this integration of competencies has broader implications for understanding expertise and expert development and may inform pedagogical practices that promote the development of complex problem solving in trainees.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.023 |
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".