The Role of Emotion in the Learning and Transfer of Clinical Skills and Knowledge
Bibliographic record
Abstract
PURPOSE: Medical school and residency are emotional experiences for trainees. Most research examining emotion in medicine has focused on negative moods associated with physician burnout and poor quality of life. However, positive emotional states also may have important influences on student learning and performance. The authors present a review of the literature on the influence of emotion on cognition, specifically how individuals learn complex skills and knowledge and how they transfer that information to new scenarios. METHOD: From September 2011 to February 2012, the authors searched Medline, PsycInfo, GoogleScholar, ERIC, and Web of Science, as well as the reference lists of relevant articles, for research on the interaction between emotion, learning, and knowledge transfer. They extracted representative themes and noted particularly relevant empirical findings. RESULTS: The authors found articles that show that emotion influences various cognitive processes that are involved in the acquisition and transfer of knowledge and skills. More specifically, emotion influences how individuals identify and perceive information, how they interpret it, and how they act on the information available in learning and practice situations. CONCLUSIONS: There are many ways in which emotions may influence medical education. Researchers must further explore the implications of these findings to ensure that learning is not treated simply as a rational, mechanistic process but that trainees are effectively prepared to perform under a wide range of emotional conditions.
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 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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".