New Directions in Medical e-Curricula and the Use of Digital Repositories
Bibliographic record
Abstract
Medical educators involved in the growth of multimedia-enhanced e-curricula are increasingly aware of the need for digital repositories to catalogue, store and ensure access to learning objects that are integrated within their online material. The experience at the Faculty of Medicine at McGill University during initial development of a mainstream electronic curriculum reflects this growing recognition that repositories can facilitate the development of a more comprehensive as well as effective electronic curricula. Also, digital repositories can help to ensure efficient utilization of resources through the use, re-use, and reprocessing of multimedia learning, addressing the potential for collaboration among repositories and increasing available material exponentially. The authors review different approaches to the development of a digital repository application, as well as global and specific issues that should be examined in the initial requirements definition and development phase, to ensure current initiatives meet long-term requirements. Often, decisions regarding creation of e-curricula and associated digital repositories are left to interested faculty and their individual development teams. However, the development of an e-curricula and digital repository is not predominantly a technical exercise, but rather one that affects global pedagogical strategies and curricular content and involves a commitment of large-scale resources. Outcomes of these decisions can have long-term consequences and as such, should involve faculty at the highest levels including the dean.
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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 | 0.045 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".