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Record W3095879057 · doi:10.1515/ijnes-2020-0074

Seeking transformation: how students in nursing view their academic writing context – a qualitative systematic review

2020· review· en· W3095879057 on OpenAlexaff
Kim Mitchell, Laurie Blanchard, Tara Roberts

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2020
Typereview
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsUniversity of ManitobaRed River College
Fundersnot available
KeywordsTransformative learningContext (archaeology)Grading (engineering)Qualitative researchAcademic writingMEDLINENurse educationMedical educationPsychologyPedagogyNursingMedicineSociologySocial science

Abstract

fetched live from OpenAlex

Writing practices in nursing education programs are situated in a tension-filled context resulting from competing medical-technical and relational nursing discourses. The goal of this qualitative meta-study is to understand, from the student perspective, how the context for writing in nursing is constructed and the benefits of writing to nursing knowledge development. A literature search using the CINHAL, Medline, ERIC, and Academic Search complete databases, using systematic methods identified 21 papers and dissertations which gathered qualitative interview or survey data from students in nursing at the pre-registration, continuing education, and graduate levels. The studies provided evidence that writing assignments promote professional identity development but overemphasis on writing mechanics when grading have a deleterious effect on learning and student engagement with writing. Relationship building with faculty should extend beyond what is needed to maximize grades. Suggestions for writing pedagogical reform are identified to facilitate a change in focus from mechanical-technical to transformative writing.

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)0.039
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.074
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0120.012
Science and technology studies0.0020.002
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.201
GPT teacher head0.495
Teacher spread0.294 · 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.

Study designSystematic review
DomainMethods
GenreReview

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

Citations12
Published2020
Admission routes1
Has abstractyes

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