A website to support EBM instruction for medical students : Faculty-librarian collaboration
Bibliographic record
Abstract
In the context of a fully electronic curriculum, a website was developed to present all the required materials for a medical student’s intensive course in evidence based medicine. The aim was to make available all didactic and administrative materials on a single, user-friendly website. The website was developed by library staff over a period of four years in collaboration with administrators and instructors in the Faculty of Medicine at McGill University and includes all course materials, evaluation processes, information for students, and information resources. The course is coordinated by a team of five individuals, one of whom is a health sciences librarian.\nThe site is online at http://www.health.library.mcgill.ca/ebm. Course Materials includes all lecture and workshop slide presentations as well as an online version of the course text and online access to additional readings. Evaluation Processes includes tutorial assignments, online quizzes, and a final assignment. Information for Students includes all scheduling, information about the instructors, evaluation, and organizational matters. Information Resources includes a selected collection of EBM resources, access to specialized databases such as the Cochrane Library, bibliographies, links to EBM centres and directories, EBM journals, and links to sources for Clinical Practice Guidelines. This component represents the significant input of the professional librarian.\nThe site is revised annually as the course evolves and as newer evidence based resources become available on the Internet. Recent evolution and adaptation has focused on improving usability and on providing online access to the course text.
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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 | 0.036 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.054 |
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".