Preprints in Health Professions Education: Raising Awareness and Shifting Culture
Bibliographic record
Abstract
A preprint is a version of a research manuscript posted by its authors to a preprint server before peer review. Preprints are associated with a variety of benefits, including the ability to rapidly communicate research, the opportunity for researchers to receive feedback and raise awareness of their research, and broad and unrestricted access. For early-career researchers, preprints also provide a mechanism for demonstrating research progress and productivity without the lengthy timelines of traditional journal publishing. Despite these benefits, few health professions education (HPE) research articles are deposited as preprints, suggesting that preprinting is not currently integrated into HPE culture. In this article, the authors describe preprints, their benefits and related risks, and the potential barriers that hamper their widespread use within HPE. In particular, the authors propose the barriers of discordant messaging and the lack of formal and informal education on how to deposit, critically appraise, and use preprints. To mitigate these barriers, several recommendations are proposed to facilitate preprints in becoming an accepted and encouraged component of HPE culture, allowing the field to take full advantage of this evolving form of research dissemination.
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 | 0.143 | 0.432 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.031 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".