Research Briefs as Communication and Motivation Tools: Knowledge Translation in Medical Education
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Medical learners are critical stakeholders in medical education research - they are both research participants and end-users of research findings. Traditional forms of disseminating research findings may take years to produce and may never be accessed by participants. Despite this, medical education researchers are responsible for ensuring that research findings reach medical learners faster and more directly. As such, Research Briefs can be a useful vehicle for communicating research findings, rewarding participation in research, and supporting medical learners in their journey to become doctors. We provide examples of Research Briefs that we have developed to translate knowledge and engage medical learners in our longitudinal research study. We have used Research Briefs to communicate our findings both to participating students and to the larger student community at our university. Doing so has allowed us to start raising awareness of the roles motivation and coping - specifically, achievement goals, self-compassion, and physical activity - play in the learning and well-being of our students.
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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 | 0.160 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".