Affective dimensions of academic librarians’ experiences during the Covid-19 pandemic: experiences and lessons learned for information literacy
Bibliographic record
Abstract
Purpose This article explores the affective dimensions of academic librarians’ experiences during the forced pivot to emergency remote teaching because of the Covid-19 pandemic. Design/methodology/approach Through semi-structured interviews with librarians at 18 university libraries in Ontario, Canada, the researcher prompted study participants to reflect on how their work and that of other librarians in their organization changed during the period of focus, including the main challenges and opportunities experienced for information literacy instruction. Findings This study finds evidence of stress and anxiety among academic librarians teaching during the Covid-19 pandemic, including lack of confidence and skills with eLearning and work-life balance challenges. At the same time, the data show strengths and successes fueled by resilience, collaboration and a growing culture of care, which in many cases, resulted in strong expressions of pride by interviewees on what was achieved during this global health crisis. Originality/value This study is one of few adopting a qualitative research methodology to explore the affective dimensions of academic librarians’ experience of information literacy instruction during the Covid-19 pandemic. Its implications are instructive for future pedagogical approaches and workplace culture among information literacy teams, including communication, collaboration, flexibility and leadership support.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".