Bibliographic record
Abstract
A Review of: Lundstrom, K., Fagerheim, B. & Van Geem, S. (2021). Library teaching anxiety: Understanding and supporting a persistent issue in librarianship. College & Research Libraries, 82(3), 389-409. https://doi.org/10.5860/crl.82.3.389 Abstract Objective – To determine academic librarians’ attitudes towards their teaching, how teaching anxiety manifests itself, and how teaching anxiety affects these attitudes. Design – Online Survey. Setting – The survey was distributed through various library science listservs. Subjects – Any library staff with a teaching component in their role were invited to respond. There was a total of 1,035 initial responses. Methods – The survey questions were based on a previously published survey about teaching anxiety by Davis (2007). However, the survey for this study added questions about formal and self-diagnosis of other types of anxieties, physical and psychological anxiety symptoms, and how teaching anxiety impacts other areas of the respondents’ lives. There were also questions on potential supports to reduce teaching anxiety, as well as potential barriers to these supports. Main Results – It was found that approximately 65% of respondents experience teaching anxiety. Approximately 40% of those respondents were formally diagnosed with anxiety, and approximately 42% were self-diagnosed. There was a significant association between a formal diagnosis of anxiety, and teaching anxiety. There were also significant associations between past training, preparation, and teaching anxiety, with anxiety occurring less with increased training and preparation. Conclusion – Teaching anxiety is a significant issue among library staff. Supports in the form of workshops on teaching as well as coping with anxiety can possibly help to reduce this phenomenon.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.018 |
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".