Health sciences and medical librarians conducting research and their experiences asking for co-authorship
Bibliographic record
Abstract
Objective: Health sciences librarians frequently engage in scholarly publication, both with other librarians undertaking intradisciplinary scholarship, and increasingly as members of research teams centered in other disciplines. We sought to assess the emotional and institutional context of authorship among health sciences librarians, including emotions experienced during authorship negotiation, the frequency with which authorship is denied, and the correlation of perceived support from supervisors and the research community with the number of publications produced. Methods: 342 medical and health sciences librarians took an online survey of 47 questions regarding emotions experienced when asking for authorship, denial of authorship, if they have been given authorship without asking, and the extent to which they felt supported to conduct research in their current job. Results: Authorship negotiation creates varied and complex emotions among librarians. The emotions reported differed when negotiating authorship with librarian colleagues and when negotiating authorship with professionals in another field. Negative emotions were reported when asking either type of colleague for authorship. Respondents reported feeling mostly supported and encouraged by their supervisors, research communities, and workplaces. Nearly one quarter (24.4%) of respondents reported being denied authorship by colleagues outside of their departments. Perceived research appreciation and support by the research community is correlated with the total number of articles or publications produced by librarians. Conclusion: Authorship negotiation among health sciences librarians involves complex and frequently negative emotions. Denial of authorship is frequently reported. Institutional and professional support appear to be critical to publication among health sciences librarians.
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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 | 0.021 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".