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Record W2206910979 · doi:10.5430/jnep.v6n4p117

Creating and evaluating a global classroom to teach nursing research

2015· article· en· W2206910979 on OpenAlexvenueno aff
Barbara Amendolia, Kathleen Fisher, Deanna Lynn Schaffer, Kathryn Howarth

Bibliographic record

VenueJournal of Nursing Education and Practice · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsEnthusiasmNursing researchNursingNurse educationScope (computer science)LicensureMedical educationMedicinePsychologyPedagogyComputer science

Abstract

fetched live from OpenAlex

Background: The purpose of this educational innovation was to expose undergraduate nursing students to methods of research and evidence-based practice through dissemination at a research conference and interaction with nurse researchers. Methods: Students from the United States attended an interdisciplinary research conference in Dublin Ireland that integrated the research process into their nursing education. In addition, they were exposed to a very different approach to nursing education through interaction within a global classroom. Results: Following the experience, students expressed a greater understanding of the overall research process. Students were both surprised and impressed that bedside nurses could be actively engaged in research and could envision nursing research as a component of their scope of practice. Conclusions: Infusion of research into pre-licensure programs remains a challenge for nursing faculty. International experiences and global classrooms can be a way to meet this challenge and raise enthusiasm about nursing research.

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)0.060
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.057
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0110.007
Open science0.0030.019
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.432
GPT teacher head0.639
Teacher spread0.206 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2015
Admission routes1
Has abstractyes

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