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Record W2584688536 · doi:10.5430/jnep.v7n6p95

A novel program for ABSN students to generate interest in geriatrics and geriatric nursing research

2017· article· en· W2584688536 on OpenAlexvenueno aff
Jennifer Mewshaw, Donald E. Bailey, Amber L. Anderson, Ruth A. Anderson, Andrew Burd, Cathleen Colón‐Emeric, Kirsten Corazzini

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersNational Institute of Nursing ResearchNational Institute on Aging
KeywordsInternshipGeriatricsNursingEconomic shortageGerontological nursingMedicineMedical education

Abstract

fetched live from OpenAlex

The current shortage of nurse researchers in geriatrics adversely affects the capacity of nurses to conduct research to advance the evidence-based care of older adults. In an effort to generate interest in geriatrics and geriatric nursing research, the Duke University School of Nursing designed a summer internship for four students enrolled in the accelerated baccalaureate nursing (ABSN) program. This paper describes the experience of these ABSN students and the staff and faculty who worked with them. The program design, staff and faculty experiences, benefits and challenges, as well as recommendations for future programs are discussed. The purpose of this article is to highlight the benefits and challenges of offering research experiences to nursing students in an ABSN program to stimulate interest in geriatrics and geriatric 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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0070.002
Scholarly communication0.0030.002
Open science0.0020.012
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0160.004

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 head0.451
GPT teacher head0.648
Teacher spread0.198 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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