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Record W3060317366 · doi:10.5430/jnep.v10n12p1

Junior baccalaureate nursing students: Factors that predict success

2020· article· en· W3060317366 on OpenAlexvenueno aff
Ellen M.T. Smith

Bibliographic record

VenueJournal of Nursing Education and Practice · 2020
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceBaccalaureate DegreeMedical educationNursingAcademic achievementPsychologyNurse educationMedicineHigher educationPedagogy

Abstract

fetched live from OpenAlex

Baccalaureate nursing education strives toward comprehensive preparation of diverse nursing students to meet current healthcare workforce demands. Identification of factors that predict academic success is imperative to meet this goal. The purpose of this study was to discover whether specific academic and noncognitive variables predicted baccalaureate nursing students’ academic success, as defined by junior-year grade point average (GPA) and persistence in nursing education. This post-facto correlational study was conducted over two semesters. Junior year nursing students (N = 150) answered the Short Grit Survey and the Noncognitive Questionnaire, and their academic records were examined for previous college grades (GPAs) and SAT scores. Demographic groups were compared using t-tests, and the data were regressed on junior-year student GPAs and persistence in the major to determine predictors of success. Several significant differences between the participant group responses were noted. Only early-college GPAs predicted junior-year success. SAT scores, grit and noncognitive factors, as well as demographic variables, did not predict academic success. These results inform baccalaureate education programs about priorities for admitting and advising students, and support the use of early-college GPAs to predict the academic success of junior-year baccalaureate nursing students.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.727
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.139
GPT teacher head0.469
Teacher spread0.329 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

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