Bibliographic record
Abstract
A great deal of effort has been expended on trying to determine whether traditional instruction, online learning, or some combination of the two is of greater educational effectiveness.1, 2 This may be the wrong question. Rather than determining whether one delivery mechanism is superior to another, it is more important to choose the best method(s) of delivery to achieve specific educational objectives, taking into consideration the different learning styles and circumstances of the target audience. Although appropriate use of technology offers potential for improved learning, there are other compelling reasons to use technology in medical education, such as reaching a different audience or accessing unique expertise that would otherwise be unavailable. This article explores the ramifications of three important drivers of technology adoption for medical education: (1) public access to medical knowledge on the Internet; (2) change in what constitutes medical knowledge; and (3) the impending shortage of educators. Some approaches are proposed, including veterinary examples, to the challenges presented by these changes.
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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".