From problem solving to problem definition: scrutinizing the complex nature of clinical practice
Bibliographic record
Abstract
In medical education, we have tended to present problems as being singular, stable, and solvable. Problem solving has, therefore, drawn much of medical education researchers' attention. This focus has been important but it is limited in terms of preparing clinicians to deal with the complexity of the 21st century healthcare system in which they will provide team-based care for patients with complex medical illness. In this paper, we use the Soft Systems Engineering principles to introduce the idea that in complex, team-based situations, problems usually involve divergent views and evolve with multiple solution iterations. As such we need to shift the conversation from (1) problem solving to problem definition, and (2) from a problem definition derived exclusively at the level of the individual to a definition derived at the level of the situation in which the problem is manifested. Embracing such a focus on problem definition will enable us to advocate for novel educational practices that will equip trainees to effectively manage the problems they will encounter in complex, team-based healthcare.
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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 | 0.002 | 0.384 |
| 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 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".