Bibliographic record
Abstract
Medical education has seen significant progress and innovation over the last decade.Today's students utilise a variety of contemporary devices that have replaced the good old pencil and pen.During preclinical years, the students carry around tablet-PCs instead of notebooks to access the web-based curriculum.In their clinical years, smartphones have largely replaced reference books.As much as the teaching methods have revolutionised in medical education, one reality remains constant: during their first two years of study, medical students need to absorb a tremendous volume of information.Students are further challenged by a lack of study time prior to writing summative examinations.Podcasting is a method for distributing multimedia audio and video files over the Internet using the Really Simple Syndication (RSS) format; these can be played back on mobile devices and personal computers.RSS is a web feed format used to publish frequently updated content on the web.In implementing an educational podcasting project, the investigator recommends following the five steps of the Instructional Design Process: Define, Design, Develop, Delivery, and Demonstrate.The following tips are intended to help the reader with design, production and publication of a successful educational podcast.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.068 | 0.053 |
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".