Library Anxiety Impedes College Students’ Library Use, but May Be Alleviated Through Improved Bibliographic Instruction
Bibliographic record
Abstract
A Review of: Jiao, Q. G., Onwuegbuzie, A. J., & Lichtenstein, A. A. (1996). Library anxiety: Characteristics of ‘at-risk’ college students. Library & Information Science Research, 18(2), 151-163. https://doi.org/10.1016/S0740-8188(96)90017-1 Abstract Objective – To identify the characteristics of college students that are related to their experiences of library anxiety. Design – Survey, analyzed with multiple regression. Setting – Two universities, one in the mid-south and one in the northeastern United States. Subjects – 493 students from those two universities. Methods – The students responded to two questionnaires: the Library Anxiety Scale developed by Bostick (1992), and a Demographic Information Form that included questions about students’ gender, age, native language, academic standing and study habits, library instruction received, and library use. Spearman’s rank correlation was used to identify those demographic characteristics that were correlated with library anxiety. Multiple regression analysis was used to develop a model for predicting library anxiety. Main Results – The study found that age, sex, native language, grade point average, employment status, frequency of library visits, and reasons for using the library contributed significantly to predicting library anxiety. Library anxiety was highest among young male students who did not speak English as their native language, had high levels of academic achievement, were employed while in school, and infrequently visited the library. While the overall regression model was statistically significant and explained 21% of the variability in library anxiety, the individual correlations with library anxiety were generally weak (the strongest was a -0.21 correlation with frequency of library visits). Conclusion – The authors conclude that many students experience library anxiety, and recommend that libraries make every effort to be welcoming. In addition, they recommend that library instruction should be introduced at the high school level and, in college, incorporated into the classes that require library research. In this setting, library anxiety should be addressed during the instruction, and classroom teachers should plan to assist students in the early stages of their research.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".