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Record W4236430782 · doi:10.5195/jmla.2016.139

Impact of librarians on reporting of the literature searching component of pediatric systematic reviews

2017· article· en· W4236430782 on OpenAlexafffund
Deborah Meert, Nazi Torabi, John Costella

Bibliographic record

VenueJournal of the Medical Library Association JMLA · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcGill University
FundersCanadian Association of Research LibrariesAssociation of Research LibrariesMedical Library Association
KeywordsSystematic reviewChecklistMEDLINEIdentification (biology)MedicinePsychologyLibrary scienceComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Objective: A critical element in conducting a systematic review is the identification of studies. To date, very little empirical evidence has been reported on whether the presence of a librarian or information professional can contribute to the quality of the final product. The goal of this study was to compare the reporting rigor of the literature searching component of systematic reviews with and without the help of a librarian.Method: Systematic reviews published from 2002 to 2011 in the twenty highest impact factor pediatrics journals were collected from MEDLINE. Corresponding authors were contacted via an email survey to determine if a librarian was involved, the role that the librarian played, and functions that the librarian performed. The reviews were scored independently by two reviewers using a fifteen-item checklist.Results: There were 186 reviews that met the inclusion criteria, and 44% of the authors indicated the involvement of a librarian in conducting the systematic review. With the presence of a librarian as coauthor or team member, the mean checklist score was 8.40, compared to 6.61 (p<0.001) for reviews without a librarian.Conclusions: Findings indicate that having a librarian as a coauthor or team member correlates with a higher score in the literature searching component of systematic reviews.

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 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)0.164
metaresearch head score (Gemma)0.471
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1640.471
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0050.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.510
GPT teacher head0.510
Teacher spread0.000 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
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

Citations16
Published2017
Admission routes2
Has abstractyes

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