Publication Output and Trends of LIS Faculty Teaching Health-Related Courses: Connecting Research, Teaching, and Practice
Bibliographic record
Abstract
The publication output of Library and Information Science (LIS) faculty teaching health courses has not been analyzed. The purpose of this bibliometric analysis was to examine publication patterns of full-time LIS faculty that teach health-related courses for library science programs in the United States and Canada. Full-time LIS faculty teaching health-related courses in American Library Association (ALA)–accredited programs were identified by searching course listings, faculty profiles, and syllabi from ALA-accredited school websites and contacting deans and directors of schools. The 29 LIS faculty that were identified and met the inclusion criteria were contacted via email in September 2021 and invited to voluntarily share their curricula vitae (CVs) for analysis. A total of 16 respondents provided their CVs, representing a 55% response rate. This was supplemented by locating five more CVs publicly available online. The final sample of LIS faculty was 21, and the bibliometrics analysis was based on a total of 716 publications published from 2011 to 2021 and reported on the CVs from this group of scholars. This analysis resulted in the identification of several patterns. Journal articles were the most common publication type, followed by conference proceedings. Joint authorship patterns were more common than solo authors, highlighting the collaborative nature of research. While faculty published in a range of LIS and interdisciplinary journals, highly cited papers appeared in health specialty journals. This study represents the first step in examining the research output for this under-explored community of LIS scholars. These findings may be of interest to promotion and tenure committees, newer tenure-track faculty, and doctoral students exploring academic careers in this specialized area.
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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 | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.050 | 0.097 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".