From understanding to embracing: A guide on emotions in medical education research: AMEE Guide No. 184
Bibliographic record
Abstract
Emotions refer to conscious and subjectively experienced mental reactions that are often associated with physiological and behavioral changes. In the context of medical education research, emotions have a pervasive influence on how various types of information are perceived and processed, and therefore, can influence how research is designed, conducted, and implemented. While there is considerable research on how emotions affect learning, there is little guidance for researchers on how to recognize and potentially leverage emotions while conducting and disseminating medical education research. Emotions can be potentially beneficial for fostering a stronger connection to research, increasing motivation to conduct sensitive research, and enhancing reflexivity and rigor. In this guide, the authors describe how emotions may influence medical education research while assisting researchers on how to recognize and manage emotions during the research process. This guide builds upon existing research to provide a framework for emotional reflexivity in the context of medical education research.
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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.025 | 0.028 |
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".