University Instructors Use of, and Satisfaction with, Library Services in the Year Following the COVID-19 Outbreak
Bibliographic record
Abstract
A Review of: McClure, J. (2023). The COVID-19 pandemic and the rapid shift to an exclusively online format: Tracking online instructors’ utilization of library services over a year of virtual learning at the University of Memphis. College & Research Libraries, 84(1), 100–120. https://doi.org/10.5860/crl.84.1.100 Objective – To determine online instructors' satisfaction with and level of use of library services during the year following the onset of the COVID-19 pandemic. Design – Survey questionnaire and follow-up interview. Setting – The University of Memphis (UofM) is a medium-sized, public, urban, R2 (doctoral university with high research activity) university. At the time of publication, UofM employed 930 full-time faculty and—through UofM Global—the option to earn degrees entirely online. Subjects – Survey respondents (n = 56) were online instructors at the University of Memphis. Methods – A confidential survey was distributed to all deans and department chairs at the UofM with instructions to disseminate the survey to all instructors teaching online course(s). Respondents were invited to participate in a follow-up interview. Main Results – Three common themes identified from the data were 1) respondents would have used the enhanced library services but were not aware of them; 2) respondents were very grateful for the services offered, in particular library instruction, Kanopy, and interlibrary loan; and 3) respondents did not feel like their courses would benefit from the library or its offered services. Conclusion – Based on the research results, the author concludes that the UofM Library must focus efforts on increasing visibility and communication, embedding the library in course design and assessment, as well as improving hybrid library instruction and offering purchase-on-demand collection development. The author has begun work on a follow-up study looking at ways to enhance the embedded librarianship service and increase communication between the UofM librarians and online teaching faculty.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".