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Technology in nursing scholarship: Use of citation reference managers

2007· article· en· W2027941987 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

VenueInternational Journal of Mental Health Nursing · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsConsistency (knowledge bases)Variety (cybernetics)CitationComputer scienceScholarshipSoftwarePublicationProcess (computing)Data sciencePublish or perishWorld Wide WebPublishingBusinessPolitical science

Abstract

fetched live from OpenAlex

Nurses, especially those in academia, feel the pressure to publish but have a limited time to write. One of the more time-consuming and frustrating tasks of research, and subsequent publications, is the collection and organization of accurate citations of sources of information. The purpose of this article is to discuss three types of citation reference managers (personal bibliographic software) and how their use can provide consistency and accuracy in recording all the information needed for the research and writing process. The advantages and disadvantages of three software programs, EndNote, Reference Manager, and ProCite, are discussed. These three software products have a variety of options that can be used in personal data management to assist researchers in becoming published authors.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.386
Teacher spread0.307 · 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