In search of educational efficiency: 30 years of <i>Medical Education</i> 's top‐cited articles
Bibliographic record
Abstract
CONTEXT: Academic journals represent shared spaces wherein the significance of thematic areas, methodologies and paradigms are debated and shaped through collective engagement. By studying journals in their historical and cultural contexts, the academic community can gain insight into the ways in which authors and audiences propose, develop, harness, revise and discard research subjects, methodologies and practices. METHODS: Thirty top-cited articles published in Medical Education between 1986 and 2014 were analysed in a two-step process. First, a descriptive classification of articles allowed us to quantify the frequency of content areas over the time span studied. Secondly, a discourse analysis was conducted to identify the continuities, disruptions and tensions within the three most prominent content areas. RESULTS: The top-cited articles in Medical Education focused on three major areas of interest: problem-based learning, simulation and assessment. In each of these areas of interest, we noted a tension between the desire to produce and apply standardised tools, and the recognition that the contexts of medical education are highly variable and influenced by political and financial considerations. The general preoccupation with achieving efficiency may paradoxically jeopardise the ability of medical schools to address the contextual needs of students, teachers and patients. CONCLUSIONS: Understanding the topics of interest for a journal's scholarly audience and how these topics are discursively positioned, provides important information for researchers in deciding how they wish to engage with the field, as well as for educators as they assess the relevance of educational products for their local contexts.
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.040 | 0.038 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".