An International, Cross-Sectional Survey of Preprinting Attitudes Among Biomedical Researchers
Bibliographic record
Abstract
Abstract Background Preprints are scientific manuscripts that are made available on open-access servers but are not yet peer reviewed. While preprints are becoming more prevalent uptake is not uniform or optimal. Understanding researchers’ opinions and attitudes towards preprints is valuable to their successful implementation. Understanding knowledge gaps and researchers’ attitudes toward preprinting can assist stakeholders like journals, funding agencies, and universities to implement preprints more effectively. Here, we aim to collect perceptions and behaviours regarding preprints in across an international sample of biomedical researchers. Methods Biomedical authors were identified by a keyword-based, systematic search from the MEDLINE database, and their emails were extracted to invite them to our survey. A cross-sectional anonymous survey was distributed to all identified biomedical authors to collect their knowledge, attitudes, and opinions about preprinting. Results The survey was completed by 730 biomedical researchers with a response rate of 3.20% and demonstrated a wide range of attitudes and opinions about preprints with authors from various disciplines and career stages around the world. Most respondents were familiar with the concept of preprints, but most had not published a preprint before. The lead author of the project and journal policy had the most impact on decisions to post a preprint, while employers/research institute had the least impact. Supporting open science practices was the highest ranked incentive, while increases to authors’ visibility was highest ranked motivation for publishing preprints. Conclusion While many biomedical researchers recognize the benefits of preprints, there is still hesitation among others to engage in this practice. This may be due to the general lack of peer review of preprints and little enthusiasm from external organizations, such as journals, funding agencies, and universities. Future work is needed to determine optimal ways to increase researcher’s attitudes through modifications to current preprint systems and policies.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".