From struggle to strength: Embracing productive failure in clinical learning
Bibliographic record
Abstract
It has become increasingly apparent that healthcare is characterized by a great degree of novelty, ambiguity, and complexity. Traditional approaches to education in the health professions that emphasize the acquisition and assessment of isolated knowledge and skills as the gold standard do not prepare learners to adapt, innovate, and continue to learn throughout their careers. Productive failure, originating in the adaptive expertise literature, is an evidence-based instructional approach that has been shown to prepare students across the continuum of education for future learning. However, while productive failure is frequently used in classrooms, less is known about how to apply this approach in a clinical learning environment where learners are expected to learn while also fulfilling the work expectations of taking care of patients. In this paper, we define productive failure, describe research on how productive failure supports the development of adaptive expertise, and introduce the pedagogical implications of this work for educators, learners, administrators and patients. Drawing on cognitive and socio-culture research, and insights from quality improvement strategies, we will discuss how the clinical learning environment can be leveraged to ensure that learning through struggle is productive and safe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".