A cross-sectional survey on academic librarian involvement in evidence-based medicine instruction within undergraduate medical education programs in Canada
Bibliographic record
Abstract
Introduction: The purpose of this study was to determine the range of involvement of Canadian academic medical librarians in teaching evidence-based medicine (EBM) within the undergraduate medical education (UME) curriculum. This study articulates the various roles that Canadian librarians play in teaching EBM within the UME curriculum, and also highlights their teaching practices. Methods: An electronic survey was distributed to a targeted sample of academic librarians currently involved in UME programs in Canadian medical schools. Results: 12 respondents (including one duplicate response) representing ten schools responded to this survey. 7 of 10 respondents were involved in EBM instruction, 3 of 10 institutions had a dedicated EBM course. Librarians were involved in a variety of roles, and often co-created and co-delivered content along with medical school faculty, and were present on course committees. They used a variety of educational strategies, incorporated active learning, as well as online modules. Discussion/Conclusion: The data highlighted the embedded nature of EBM instruction in undergraduate medical education programs in Canada. It also showed that librarians are involved in EBM instruction beyond the second step of EBM; acquiring or searching the literature.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".