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Record W2797238071 · doi:10.5430/jnep.v8n8p119

Finnish graduating nursing students’ research utilization competence

2018· article· en· W2797238071 on OpenAlexvenueno aff
Asta Heikkilä, Maija Hupli, Jouko Katajisto, Helena Leino‐Kilpi

Bibliographic record

VenueJournal of Nursing Education and Practice · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)CurriculumNursingCore competencyMedical educationPsychologyNurse educationDescriptive researchMedicinePedagogySociology

Abstract

fetched live from OpenAlex

In nursing, research utilization (RU) is a core competence for evidence-based practice (EBP). During the past fifteen years, a great deal of effort has been expended worldwide in nursing higher education to promote EBP. This study explores graduating nursing students’ RU competence in Finland using a descriptive cross-sectional, long-term survey design with two cohorts of nursing students in 2003 (n = 529) and 2012 (n = 259). Data were collected with a Competence in Research Utilization instrument, and analyzed statistically. In both cohorts, students’ attitudes towards RU were positive, but their knowledge and skills were low to moderate. Students’ RU competence was higher in 2003 compared to 2012. There is a need to develop nursing education strategically, and by seeking suitable pedagogical methods and curriculum contents to support the learning of RU. In higher education, educational cooperation and longitudinal learning outcome evaluations are recommended.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.719
GPT teacher head0.727
Teacher spread0.008 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
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

Citations8
Published2018
Admission routes1
Has abstractyes

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