Global Scientific Trends on Library Anxiety from 1927 to 2025
Bibliographic record
Abstract
This study examines the global scientific literature on library anxiety. This study employs bibliometric analysis using the Lens.org database. Using the lens.org database, 560 data points were extracted. The study examines author productivity, journal productivity, and the correlation between PlumX metrics. The study uses R Studio, SPSS, and Lens.org for performance and science mapping analysis. The finding reveals a significant growth in research on library anxiety literature, reflecting growing scholarly interest in this domain. Anthony J. Onwuegbuzie was the most prolific author, Nature was the most productive journal, while Springer Science and Business Media LLC was the leading publisher, with 156 articles. The PlumX metric analysis demonstrated a strong correlation between citation counts, captures, and mentions but no significant relationship with article usage or social media activity. The United States dominated library anxiety research, followed by Canada and Australia. This research offers a systematic bibliometric and altmetric analysis of library anxiety research. The research presents new information on research trends, influential authors, and usage patterns, which can guide subsequent studies and library management practices for improving the student experience.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".