Characterizing the literature on validity and assessment in medical education: a bibliometric study
Bibliographic record
Abstract
INTRODUCTION: Assessment in Medical Education fills many roles and is under constant scrutiny. Assessments must be of good quality, and supported by validity evidence. Given the high-stakes consequences of assessment, and the many audiences within medical education (e. g., training level, specialty-specific), we set out to document the breadth, scope, and characteristics of the literature reporting on validation of assessments within medical education. METHOD: Searches in Medline (Ovid), Web of Science, ERIC, EMBASE (Ovid), and PsycINFO (Ovid) identified articles reporting on assessment of learners in medical education published since 1999. Included articles were coded for geographic origin, journal, journal category, targeted assessment, and authors. A map of collaborations between prolific authors was generated. RESULTS: A total of 2,863 articles were included. The majority of articles were from the United States, with Canada producing the most articles per medical school. Most articles were published in journals with medical categorizations (73.1% of articles), but Medical Education was the most represented journal (7.4% of articles). Articles reported on a variety of assessment tools and approaches, and 89 prolific authors were identified, with a total of 228 collaborative links. DISCUSSION: Literature reporting on validation of assessments in medical education is heterogeneous. Literature is produced by a broad array of authors and collaborative networks, reported to a broad audience, and is primarily generated in North American and European contexts. Our findings speak to the heterogeneity of the medical education literature on assessment validation, and suggest that this heterogeneity may stem, at least in part, from differences in constructs measured, assessment purposes, or conceptualizations of validity.
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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 | 0.078 | 0.343 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.193 | 0.191 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".