Bibliographic record
Abstract
A number of medical schools in Korea have been using computer-based testing (CBT) for evaluating their students’ scientific and/or clinical performance since the early 1990s. Introducing CBT to medical education would have several advantages: first, presenting figures and audio-video files of clinical content is simple with CBT, making it possible to evaluate medical students’ competency with navigating more realistic clinical situations at minimum cost; second, CBT enables automatic item analysis and score reporting. To establish CBT, constructing an item bank with item parameters such as difficulty or discriminating parameters will be needed. To select more psychometrically sound items, analysis of the items according to item response theory is necessary. CBT has already been introduced in high stakes tests like the United States Medical Licensing Examination and the Medical Council of Canada Qualifying Examination. The National Health Personnel Examination Board in Korea is also planning to introduce a CBT-based version of the National Medical Examination soon. Thus all medical schools in Korea will need to introduce CBT and construct item banks to prepare their students for their licensing examinations and to measure the students’ competency more accurately
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.257 | 0.110 |
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".