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Record W2946677954 · doi:10.5430/jnep.v9n8p121

Nurturing academic writing for students in an enrolled to registered nurse conversion course at university

2019· article· en· W2946677954 on OpenAlexvenueno aff
Elizabeth Emmanuel, Marilyn Chaseling

Bibliographic record

VenueJournal of Nursing Education and Practice · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsBachelorMedical educationAcademic yearNurse educatorPsychologyVocational educationRegistered nurseNurse educationMedicineMathematics educationNursingPedagogyPolitical science

Abstract

fetched live from OpenAlex

A growing number of diploma-qualified nurses from vocational programs are enrolling in university Bachelor of Nursing programs to upgrade their qualifications. Universities typically provide these students with credit so they enter the Bachelor of Nursing program in second year. Known as pathway students, these students tend to miss the orientating opportunities that other students experience in their first-year university enrolment. This lack of first-year opportunity can be challenging for many pathway students, notably in academic writing. This paper reports on a tailored and scaffolded academic-writing teaching strategy designed for pathway students in their initial semester of learning. Both the students themselves, and teachers report evidence of improvements in academic writing amongst the pathway 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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.095
GPT teacher head0.497
Teacher spread0.402 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

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