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Record W1481268159 · doi:10.3928/0148-4834-20011001-06

Perspective Transformation in RN-to-BSN Distance Education

2001· article· en· W1481268159 on OpenAlexaff
Catherine E. Cragg, Ronald C. Plotnikoff, Kylie Hugo, Alberta Casey

Bibliographic record

VenueJournal of Nursing Education · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsResocializationPerspective (graphical)SocializationDistance educationMedical educationScale (ratio)PsychologyNursingMedicinePedagogyComputer scienceSociologySocial psychology

Abstract

fetched live from OpenAlex

This study examines perspective transformation (or professional resocialization) in RN-to-BSN students obtaining their degree by distance education. A socialization scale was used to compare newly admitted and graduating RN-to-BSN students who had taken their courses onsite, by distance education, or a mixture of the two methods. The scores of entering and graduating RN-to-BSN students also were compared to those of graduates from the generic program to identify if program and experience are factors in scores achieved. Results indicate that all BSN graduates had significantly higher scores than the diploma-prepared nurses entering the RN-to-BSN program. RN-to-BSN graduates who had used distance education had the highest scores, followed by the onsite RN-to-BSN students. Students who had taken a mixture of distance and onsite courses had scores similar to those of generic program graduates. Experience and full-time employment status were significantly associated with higher scores among graduating RN-to-BSN students. Implications for nurse educators working with RN-to-BSN students who use distance education are discussed.

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)0.005
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.414
Teacher spread0.390 · 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

Citations54
Published2001
Admission routes1
Has abstractyes

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