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Record W2604604230 · doi:10.5195/jmla.2017.207

Knowledge of journal impact factors among nursing faculty: a cross-sectional study

2017· article· en· W2604604230 on OpenAlexaffabout
Maha Kumaran, Chau Ha

Bibliographic record

VenueJournal of the Medical Library Association JMLA · 2017
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsSaskatchewan PolytechnicUniversity of Saskatchewan
Fundersnot available
KeywordsCross-sectional studyNursingMedicinePsychologyMedical education

Abstract

fetched live from OpenAlex

OBJECTIVE: The research assessed nursing faculty awareness and knowledge of the journal impact factor (JIF) and its impact on their publication choices. METHODS: A qualitative cross-sectional questionnaire was developed using Fluid Survey and distributed electronically to nursing faculty and instructors at three post-secondary institutions in Saskatchewan. Data were collected on place and status of employment, knowledge and awareness of JIFs, and criteria used to choose journals for publication. RESULTS: A total of forty-four nursing faculty and instructors completed the questionnaire. The authors found that faculty lack awareness or complete understanding of JIFs and that JIFs are not the most important or only criterion used when they choose a journal for publication. CONCLUSIONS: There are various reasons for choosing a journal for publication. It is important for librarians to understand faculty views of JIFs and their criteria for choosing journals for publication, so that librarians are better equipped to guide researchers in considering their academic goals, needs, and personal values.

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)0.038
metaresearch head score (Gemma)0.178
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0380.178
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.014
Science and technology studies0.0010.000
Scholarly communication0.0060.004
Open science0.0060.001
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 head0.460
GPT teacher head0.617
Teacher spread0.157 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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