Approaching Impact Meaningfully in Medical Education Research
Bibliographic record
Abstract
Medical education research faces increasing pressure to demonstrate impact and utility. These pressures arise amidst a climate of accountability and within a culture of outcome measurement. Conventional metrics for assessing research impact such as citation analysis have been adopted in medical education, despite researchers' assertion that these quantitative measures insufficiently reflect the value of their work. Every knowledge community has its own definitions of what counts as knowledge, how that knowledge should be produced, and how the quality of that knowledge production should be evaluated. Definitions of impact and knowledge shape and constrain researchers' foci and endeavors. Therefore, metrics that meaningfully evaluate the knowledge outputs of researchers need to be defined within each field. It is time for medical education research, as a field, to examine how to measure research impact and carefully consider the broader implications these measures may have. The authors discuss developments in research metrics more broadly, then critically examine impact metrics currently used in the medical education field and propose alternatives to more meaningfully track and represent impact in medical education research. Grey metrics and narrative impact stories to more fully capture the richness and nuanced nature of impact in medical education research are introduced. The authors advocate for a continual examination of how impact is defined, eschewing unquestioned use of conventional metrics. A new conversation is needed, as well as a research agenda to help medical education conceptualize and study metrics more appropriate for the field.
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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 | 0.562 | 0.725 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.033 | 0.030 |
| Science and technology studies | 0.013 | 0.072 |
| Scholarly communication | 0.078 | 0.096 |
| Open science | 0.007 | 0.050 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".