Bibliographic record
Abstract
This issue of ‘Really Good Stuff’ (RGS) marks the 6th year of the feature in Medical Education and a quick review of introductions I have written in the past reveal that I have maintained a fairly constant plea – that is, to have more of the world's medical schools represented in the journal, beyond the U.S., Canada, and the UK. I am pleased to report that this issue of RGS is beginning to strike a balance and include many more medical schools from around the globe. In particular, I want to highlight the work of a group of authors who took part in the International Fellowship in Medical Education (IFME) programme offered by the Foundation for Advancement of International Medical Education and Research (FAIMER). The FAIMER fellowship programmes for medical educators include The Institute and the IFME programme. The intent is to create a new educational pathway that will allow international medical educators to become outstanding local resources for improving medical education. The 5 reports that were selected for publication in this issue were submitted individually, but I have learned that the authors devoted considerable time during their FAIMER Fellowship to working on their projects and writing up the results. More information about the Fellowship programme is available at http://www.faimer.org. I chose to highlight these reports to encourage everyone to consider the projects they are working on and to think about whether others might benefit from learning more about them. Medical Education is truly an international journal. There are different educational programme structures, accreditation standards, licensure requirements, and political climates for every reader. Consequently, a topic that would be of critical importance in one country may not seem new or innovative to another reader. However, please keep in mind that by featuring a variety of approaches to address universal issues in medical education, we can all gain a better understanding of our medical education colleagues around the globe and RGS can provide as broad a perspective of really good stuff as possible. The majority of the submissions are still focused on undergraduate medical student education, but there are hopeful signs that more postgraduate and continuing professional development activities will be appearing in future issues. I am looking forward to working with the new editor of Medical Education, John McLachlan, and continue to be grateful for the excellent support and guidance I receive from Julie Brice and Liz Baker of the journal staff. As always, I encourage you to send us your good stuff.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.553 | 0.388 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".