Bibliographic record
Abstract
Introduction: Evidence-based practice is an important aspect of health science librarianship. However, good evidence-based practice can only occur if the body of evidence is also of adequate quality. By using bibliometric techniques to map the health science librarianship research field, one can better understand the properties of the evidence base in health science librarianship. Methods: The Library Literature & Information Science Full Text database was used to generate a bibliography of publications pertaining to health librarianship limited to the time span of 2012-2022. Using Excel and Microsoft Power BI, a descriptive analysis was conducted. VosViewer was used to create a subject term co-occurrence map. Results: The average number of publications per year is 207.3 and it was trending downwards for 2012-2022. The most frequently assigned subject term was "survey". The average number of authors per paper is 2.5 and was trending upwards. The subject term co-occurrence map identified 5 clusters of keywords, which were interpreted as major themes found in the body of literature. Discussion: The 5 keyword clusters were interpreted as major themes found in the body of literature. The identified themes were professional development, measuring the value output of librarian services, measuring the return on investment of library resources, improving the quality of LIS research, and outreach to other library and healthcare institutions. This depicts the health science librarianship research landscape as one of collaboration, concerned with finding ways of demonstrating value, and connecting with other types of libraries and the public.
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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.078 | 0.132 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".