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Record W3091000814 · doi:10.5195/jmla.2018.341

Librarians collaborating to teach evidence-based practice: exploring partnerships with professional organizations

2018· article· en· W3091000814 on OpenAlexfundno aff
Kerry Dhakal

Bibliographic record

VenueJournal of the Medical Library Association JMLA · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
FundersHealth CanadaCanadian Health Libraries AssociationFlorida Department of HealthAssociation of Research LibrariesAmerican Association of Colleges of PharmacyAmerican Library Association
KeywordsProfessional developmentProfessional associationEvidence-based practiceKnowledge managementMedical educationLibrary sciencePublic relationsMedicineComputer sciencePolitical scienceAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The study sought to determine if librarians are collaborating with nurses and professional nursing organizations to teach evidence-based practice (EBP) continuing education courses, workshop, classes, or other training activities. METHODS: A 15-question survey was sent to 1,845 members of the Medical Library Association through email. RESULTS: The survey was completed by 201 consenting respondents. Some respondents (37) reported having experience teaching continuing education in collaboration with professional health care organizations and 8 respondents, more specifically, reported having experience teaching EBP continuing education courses, workshops, classes, or other training activities in collaboration with professional nursing organizations. CONCLUSIONS: The survey results demonstrate that librarians are teaching continuing education classes or workshops in collaboration with professional health care organizations and reveal that there are a small number of librarians collaborating with professional nursing organizations to teach EBP continuing education courses, workshops, classes, or other training activities.

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)0.082
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: none
Teacher disagreement score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.082
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0020.000
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.183
GPT teacher head0.460
Teacher spread0.277 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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