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Record W2137716384 · doi:10.2202/1548-923x.1588

Online Post LPN to BN Students' Views of Transitioning to a New Nursing Role

2008· article· en· W2137716384 on OpenAlexaffabout
Sherri Melrose, Kathryn Gordon

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2008
Typearticle
Languageen
FieldPsychology
TopicCognitive and psychological constructs research
Canadian institutionsAthabasca University
Fundersnot available
KeywordsRepertory gridPerspective (graphical)Construct (python library)Qualitative researchPerceptionPsychologyMedical educationPersonal construct theoryNurse educationDescriptive researchNursingPedagogyMedicineSociologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Vocationally prepared nurses, most of whom are women, experience unique challenges when they enter an online university. And yet, educational research examining their perceptions about transitioning into a new professional role is limited. This qualitative descriptive study investigated beginning Post LPN to BN students' views of role differences among nurses and the process of transitioning between those roles. The project was framed from a constructivist theoretical perspective and applied repertory grid methodology from the field of personal construct psychology. Ten students from a Canadian online university were interviewed. Interview transcripts were analyzed for themes. Findings revealed that students appreciated recognition for their previous accomplishments. They valued affirmation of the unique challenges they faced. And, they associated gains from their new university education with some loss of their hands on bedside nursing role.

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)0.001
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.212
GPT teacher head0.545
Teacher spread0.333 · 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

Citations21
Published2008
Admission routes2
Has abstractyes

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