Checking Out Our Workspaces: An Analysis of Negative Work Environment and Burnout Utilizing the Negative Acts Questionnaire and the Copenhagen Burnout Inventory for Academic Librarians
Bibliographic record
Abstract
Objective – This study explored the prevalence of and relationship between bullying and burnout among academic librarians. The authors sought to examine three main factors contributing to negative workplace environment caused by bullying and incivility: (1) the employment characteristics of respondents (i.e., tenured, non-tenure track, and others), (2) librarianship as a second (or third) career, and (3) generational differences. Methods – The researchers administered a survey via professional electronic mailing lists in early spring 2023. Librarians over the age of 18 who hold a Masters of Library Science (MLS) or equivalent degree and were employed in an academic library at the time of taking the survey were eligible to participate. The Negative Acts Questionnaire-Revised (NAQ-R) was used to measure workplace bullying, and the Copenhagen Burnout Inventory (CBI) was used to measure workplace burnout. Survey results were analyzed using RStudio. Results – The responses (n = 267) showed the average bullying score was relatively low (M = 1.57, SD = 0.52), and the average burnout score was middling (M = 45.68, SD = 17.87). The correlation between the two scores was mild (r = 0.5, < 0.001). ANOVAs found no significant difference between NAQ-R scores due to employment type (tenured, non-tenure track, and others; F(6, 260) = 0.711, p = 0.641), duration of employment (F(5, 261) = 0.482, p = 0.79), career number (F(4, 262) = 0.585, p = 0.674), or generational identity (F(5, 261) = 0.0969, p = 0.627). ANOVAs found no significant difference between CBI scores due to employment type (F(6, 260) = 1.566, p = 0.157), duration of employment (F(5, 261) = 1.911, p = 0.0929), career number (F(4, 262) = 1.398, p = 0.235), or generational identity (F(5, 261) = 1.511, p = 0.187). Conclusion – Low to moderate levels of both bullying and burnout were found among academic librarians, but the correlation between the two phenomena was mild. No significant difference was found between employment characteristics, career progression (second or third career), or generational identity and the degree of bullying or burnout experienced. This lack of difference was contrary to researcher predictions and opens the door for further research and understanding of both bullying and burnout among academic 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".