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Record W2768220839 · doi:10.5430/jnep.v8n4p1

Factors associated with gerontological career choice: The role of curriculum type and students’ attitudes

2017· article· en· W2768220839 on OpenAlexvenueno aff
Jung‐Ah Lee, Dana Rose Garfin, Stephanie Vaughn, Young-Shin Lee

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
Fundersnot available
KeywordsGerontological nursingCurriculumAged careNursingGerontologyPsychologyNurse educationOlder peopleMedicinePedagogy

Abstract

fetched live from OpenAlex

Background and objective: Caring for a growing aging population presents a challenge in contemporary health care. This study aims to identify factors associated with nursing student’s career choice in older adult care and predictors of attitudes toward older adults. Such information is critical to inform effective gerontological nursing education.Methods: Undergraduate nursing students (N = 411) from three nursing schools in California participated in a cross-sectional, web-based survey.Results: In covariate-adjusted analyses, students who had prior experiences taking gerontology-related courses, working with older adults, living with older adults, being confident in providing older adults care, and having lower negative attitudes toward older adults were more likely to consider a future career in gerontological nursing. Students’ confidence in older adult care was negatively correlated with negative attitudes towards older adults.Conclusions: To increase students’ career choice in gerontology, nursing schools should provide more gerontology content in nursing curricula and explore avenues to increase student confidence in older adult care.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.235
GPT teacher head0.530
Teacher spread0.295 · 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 teacher head, 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

Citations20
Published2017
Admission routes1
Has abstractyes

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