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Record W1500722466 · doi:10.3163/1536-5050.100.2.008

Provision of pandemic disease information by health sciences librarians: a multisite comparative case series

2012· article· en· W1500722466 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Medical Library Association JMLA · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsMcGill University
FundersMedical Library Association
KeywordsPandemicBest practiceInformation needsHealth carePublic relationsComputer scienceKnowledge managementWorld Wide WebCoronavirus disease 2019 (COVID-19)MedicinePolitical scienceInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.020
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.330
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it