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Record W2041086816 · doi:10.3163/1536-5050.102.2.003

What criteria do consumer health librarians use to develop library collections? a phenomenological study

2014· article· en· W2041086816 on OpenAlexaff
Janet Papadakos, Aileen Trang, David Wiljer, Chiara Cipolat Mis, Alaina Cyr, Audrey Jusko Friedman, Mauro Mazzocut, Michelle Snow, Valeria Raivich, Pamela Catton

Bibliographic record

VenueJournal of the Medical Library Association JMLA · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Toronto
FundersNational Cancer Institute
KeywordsCollection developmentData collectionResource (disambiguation)Process (computing)Medical libraryQuality (philosophy)Library sciencePsychologyComputer scienceMedical educationMedicineSociology

Abstract

fetched live from OpenAlex

OBJECTIVES: The criteria for determining whether resources are included in consumer health library collections are summarized in institutional collection development policies (CDPs). Evidence suggests that CDPs do not adequately capture all of these criteria. The aim of this study was to describe the resource review experience of librarians and compare it to what is described in CDPs. METHODS: A phenomenological approach was used to explore and describe the process. Four consumer health librarians independently evaluated cancer-related consumer health resources and described their review process during a semi-structured telephone interview. Afterward, these librarians completed online questionnaires about their approaches to collection development. CDPs from participating libraries, interview transcripts, and questionnaire data were analyzed. Researchers summarized the findings, and participating librarians reviewed results for validation. RESULTS: Librarians all utilized similar criteria, as documented in their CDPs; however, of thirteen criteria described in the study, only four were documented in CDPs. CONCLUSIONS: CDPs for consumer health libraries may be missing important criteria that are considered integral parts of the collection development process. IMPLICATIONS: A better understanding of the criteria and contextual factors involved in the collection development process can assist with establishing high-quality consumer health library collections.

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)0.007
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0010.012
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.051
GPT teacher head0.406
Teacher spread0.355 · 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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
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

Citations10
Published2014
Admission routes1
Has abstractyes

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