Academic librarian support for patient-centred and inclusive medical education curricula: a case report
Bibliographic record
Abstract
Background: Medical educators are increasingly aware of the need for patient-centred and inclusive curricula. Collaboration paired with sound evidence can facilitate efforts in this area. Librarians are well-equipped to help move this work forward, as their skills and expertise can support educators through the process of revising learning materials that will incorporate timely and socially accountable information. Case Presentation: This case report describes an initiative at one Canadian medical school, whereby a health sciences librarian joined an interdisciplinary working group to support the updating of case-based learning materials for the undergraduate medical curriculum. These materials were revised with an anti-oppressive and patient-centred lens, and as an embedded member of the working group the librarian provided on-demand literature searches, participated in conversations regarding the importance of critical appraisal skills, and consulted on sustainable access to electronic materials used in the cases. From this experience and close collaboration, lessons which enhanced their practice and stronger relationships emerged for the librarian. Conclusions: Involving librarians' expertise in updating learning materials provides many benefits to curriculum developers and presents opportunities for liaison librarians to engage with their faculties more closely. Promoting patient-centredness and inclusivity is an ongoing process, and academic health sciences librarians can apply their expertise to curricular initiatives such as the one described here, while librarians working in clinical settings can support these efforts through specialized forms of teaching and outreach.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".