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Record W2141344643 · doi:10.29173/lirg644

Assessing the impact of evidence summaries in library and information practice

2015· article· en· W2141344643 on OpenAlexafffund
Lorie A. Kloda, Denise Koufogiannakis, Alison Brettle

Bibliographic record

VenueLibrary and Information Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of AlbertaMcGill University
FundersCanadian Association of Research LibrariesMcGill UniversityAssociation of Research Libraries
KeywordsBridging (networking)DisseminationMedical educationPsychologyLibrary scienceComputer scienceKnowledge managementMedicine

Abstract

fetched live from OpenAlex

Objective This study developed, validated and administered an instrument to investigate the impact of research evidence summaries published in the journal, Evidence Based Library and Information Practice. Methods Using the critical incident technique, this mixed methods study began by developing and testing a survey questionnaire, disseminating it to readers of the journal and conducting follow-up interviews with a subsample. Findings A total of 86 practitioners responded to the survey and 13 took part in interviews. Evidence summaries led to impact at four levels: librarian knowledge, librarian practice, workplace practice, and library users. The instrument was revised as a result of the findings. Conclusion This study provides unique insight into whether evidence summaries are an effective means of bridging the research-practice gap for the library community and its scholarly communication channels. The validated impact assessment instrument may also be adapted for other means of disseminating research in library and information practice.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.229
metaresearch head score (Gemma)0.594
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.951

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2290.594
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0120.010
Science and technology studies0.0030.002
Scholarly communication0.0100.010
Open science0.0020.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.469
GPT teacher head0.611
Teacher spread0.142 · 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

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainEvaluation
GenreEmpirical

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".

Quick stats

Citations7
Published2015
Admission routes2
Has abstractyes

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