To use or not to use: ERIC database for medical education research
Bibliographic record
Abstract
Introduction Bibliographic databases are essential research tools. In medicine, key databases are MEDLINE/PubMed, Embase, and Cochrane Central (MEC). In education, the Education Resource Information Center (ERIC) is a major database. Medical education, situated between medicine and education, has no dedicated database of its own. Many medical education researchers use MEC, some use ERIC and some do not.Methods We performed a descriptive analysis using search strategies to retrieve medical education references from MEC and ERIC. ERIC references which were duplicates with MEC references were removed. Unique ERIC references were tallied.Results Between 1977 and 2022, MEC has 359,354 unique references relevant to medical education. ERIC provided 3925 unique references for the same period, all of which would be missed by searching only MEC. The mean unique ERIC medical education references per year for all 46 years is 85 (SD = ±29), or 119 (SD = ±15) for the last 10 years from 2013 to 2022.Conclusion ERIC consistently offered a small yet significant number of unique references relevant to medical education for decades. We recommend the use of ERIC for medical education research when comprehensive literature searches are required, such as in systematic reviews, scoping reviews, evidence synthesis, or guideline development.
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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 | 0.065 | 0.356 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.050 | 0.073 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.299 | 0.122 |
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".