Health sciences library workshops in the COVID era: librarian perceptions and decision making
Bibliographic record
Abstract
Objective: We sought to determine how the COVID-19 pandemic impacted academic health sciences library workshops. We hypothesized that health sciences libraries moved workshops online during the height of the pandemic and that they continued to offer workshops virtually after restrictions were eased. Additionally, we believed that attendance increased. Methods: In March 2022, we invited 161 Association of American Health Sciences Libraries members in the US and Canada to participate in a Qualtrics survey about live workshops. Live workshops were defined as synchronous; voluntary; offered to anyone regardless of school affiliation; and not credit-bearing. Three time periods were compared, and a chi square test of association was conducted to evaluate the relationship between time period and workshop format. Results: Seventy-two of 81 respondents offered live workshops. A chi square test of association indicated a significant association between time period and primary delivery method, chi-square (4, N=206) = 136.55, p< .005. Before March 2020, 77% of respondents taught in person. During the height of the pandemic, 91% taught online and 60% noted higher attendance compared to pre-pandemic numbers. During the second half of 2021, 65% of workshops were taught online and 43% of respondents felt that attendance was higher than it was pre-pandemic. Overall workshop satisfaction was unchanged (54%) or improved (44%). Conclusion: Most health sciences librarians began offering online workshops following the onset of the COVID-19 pandemic. More than half of respondents were still teaching online in the second half of 2021. Some respondents reported increased attendance with similar levels of satisfaction.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".