Twelve tips for fostering productive struggle in the clinical learning environment
Bibliographic record
Abstract
Health professions educators are faced with the pressure of preparing learners for the complexity of patient care within a dynamic clinical learning environment (CLE). Cognition in context recognizes that cognitive learning mechanisms such as productive struggle are transformed by institutional culture, structural constraints, and positive and negative socializing experiences. These experiences are recorded by learners in their evaluation of the CLE as forms of struggle; however, the pressure to create an optimal learning environment may mean eliminating struggle from the learner experience, which could impact negatively the development of adaptive expertise. In this 12 tips paper, we address the issue of cognition in context and offer suggestions on how educators can make fulsome use of learner feedback to recognize productive struggle while addressing contextual constraints to ensure that learners are supported on their journey to become healthcare professionals.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".