Data, Big and Small: Emerging Challenges to Medical Education Scholarship
Bibliographic record
Abstract
The collection and analysis of data are central to medical education and medical education scholarship. Although the technical ability to collect more data, and medical education's dependence on data, have never been greater, it is getting harder for medical schools and educational scholars to collect and use data, particularly in terms of the regulations, security issues, and growing reluctance of learners and others to participate in data collection activities. These two countervailing trends present a growing threat to the viability of medical education scholarship. In response, there must either be a more conducive data environment for medical education scholarship or medical education must move to become less dependent on data.There is, therefore, a growing need for a system-wide correction: a shift in practice that makes data use more viable and productive while maintaining high professional standards. There are five core areas that can contribute to a system-wide correction: greater clarity over what can be used as data; greater clarity on what constitutes "good" data; changes to the ways in which data are collected; better strategic stewardship of existing data; and deliberate and strategic attention to "data readiness" in support of medical education and medical education scholarship. These solutions are primarily practical and conceptual changes in the face of what are mainly regulatory challenges. However, medical educators also need to engage with emerging areas of practice such as learning analytics, and they need to consider the shifting social contract for using data in medical education.
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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 | 0.228 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.012 | 0.050 |
| Scholarly communication | 0.043 | 0.054 |
| Open science | 0.010 | 0.031 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".