Bibliographic record
Abstract
In Reply to Farnan et al and to Lemon: We thank Farnan, Landon, and Arora for their observations on the restrictions to using social media in many hospitals; we echo their concerns that this obstructs health professionals from learning and augmenting their practices using these technologies. This relates to a broader problem that technologies used in hospitals, such as those used in e-health, are also not well aligned with learning.1 Clearly, there is a growing divergence between the learning needs of health care professionals and the pursuit of hospital IT governance. Lemon rightly raises concerns over the monetization of social media channels, something that has grown quite significantly since we posted the videos discussed in our article, and even since we submitted the manuscript of that article. At one level this may simply reflect the adage that “there’s no such thing as a free lunch.” The growing concern over the influence of commercial interactions with medical education, reflected in the development of institutional and journal conflict-of-interest policies, indicates that we need to consider that influence more directly. A simplistic perspective offers two alternatives: open but commercially tinged social media with a vast but anonymous audience, or closed and academically “clean” media targeted at a known and relatively small audience. However, given that many academic publishers are now adding advertising to their journals’ Web sites (even if it is usually for their own commercial services), these alternatives would be end points of a continuum. We agree with Lemon’s suggestion that we need “broad academic guidelines for those wishing to publish academic and education material for use on social platforms.” Finally, we share Lemon’s concerns that what constitutes an academic publication has not kept pace with the proliferation of channels and forms of publishing now available to medical educators. Principles of scrutiny and accountability are not lost in social media, but they are profoundly altered, particularly as the voice of the individual peer expert is exchanged for the many and varied voices of the crowd. Although our standards may not change, they are likely to be expressed and challenged in new and unexpected ways by social media. We see the debate on these issues as an indication of the health of scholarship in medical education rather than a portent of its imminent demise. Rachel Ellaway, PhD Assistant dean for undergraduate medical education, Northern Ontario School of Medicine, Sudbury, Ontario, Canada. David Topps, MD Professor of family medicine, University of Calgary, Alberta, Canada; [email protected] Joyce Helmer, EdD Associate professor, Division of Human Sciences, Northern Ontario School of Medicine, Sudbury, Ontario, Canada.
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 | 0.006 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.031 | 0.034 |
| Insufficient payload (model declined to judge) | 0.073 | 0.056 |
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".