Provision of pandemic disease information by health sciences librarians: a multisite comparative case series
Bibliographic record
Abstract
OBJECTIVE: The research provides an understanding of pandemic information needs and informs professional development initiatives for librarians in disaster medicine. METHODS: Utilizing a multisite, comparative case series design, the researchers conducted semi-structured interviews and examined supplementary materials in the form of organizational documents, correspondence, and websites to create a complete picture of each case. The rigor of the case series was ensured through data and investigator triangulation. Interview transcripts were coded using NVivo to identify common themes and points of comparison. RESULTS: Comparison of the four cases revealed a distinct difference between "client-initiated" and "librarian-initiated" provision of pandemic information. Librarian-initiated projects utilized social software to "push" information, whereas client-initiated projects operated within patron-determined parameters to deliver information. Health care administrators were identified as a key audience for pandemic information, and news agencies were utilized as essential information sources. Librarians' skills at evaluating available information proved crucial for selecting best-quality evidence to support administrative decision making. CONCLUSIONS: Qualitative analysis resulted in increased understanding of pandemic information needs and identified best practices for disseminating information during periods of high organizational stress caused by an influx of new cases of an unknown infectious disease.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".