Canadian academic nursing librarians: Impacts of the COVID-19 pandemic on librarianship practice
Bibliographic record
Abstract
Objective: This study explored changes in the practice of academic nursing librarianship at large Canadian universities during the COVID-19 pandemic with a particular focus on academic nursing librarians' work with nursing graduate students and nursing faculty. Methods: Semi-structured interviews were conducted with eleven academic nursing librarians about changes to their librarianship practice during the COVID-19 global pandemic. Interviews were conducted between 20 April and 14 May 2021, discussing experiences during the study period March 2020 to May 2021. Results: the benefits and challenges of remote work. Discussion: Experiences were divergent, shaped in part by the institutions' pre-pandemic practices. Additionally, some participants reported no impact of the pandemic on their research, instruction, and collaborations with nursing graduate students and nursing faculty. In particular, institutions already offering online masters programs in nursing reported less significant disruption. The temporary transition to the completely virtual library revealed benefits of online consultations, opportunities for reaching more students through asynchronous learning, the importance of relationships to nursing liaison work, and value of the flexibility to work remotely . Conclusion: The COVID-19 global pandemic continues to evolve. With a return to in-person classes at Canadian universities, there is much to learn from the experiences during the first 18 months of the pandemic.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".