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Record W2098555849 · doi:10.1111/nin.12090

Educational silos in nursing education: a critical review of practical nurse education in Canada

2014· review· en· W2098555849 on OpenAlexaffabout
Diane Butcher, Karen MacKinnon

Bibliographic record

VenueNursing Inquiry · 2014
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDisciplineNurse educationNursingCurriculumConfusionSociologyMedicinePsychologyPedagogySocial science

Abstract

fetched live from OpenAlex

Changes to practical nurse education (with expanded scopes of practice) align with the increasing need for nurses and assistive personnel in global acute care contexts. A case in point is this critical exploration of Canadian practical nursing literature, undertaken to reveal predominating discourses and relationships to nursing disciplinary knowledge. The objectives of this poststructural critical review were to identify dominant discourses in practical nurse education literature and to analyze these discourses to uncover underlying beliefs, constructed truths, assumptions, ambiguities and sources of knowledge within the discursive landscape. Predominant themes in the discourses surrounding practical nurse education included conversations about the nurse shortage, expanded roles, collaboration, evidence-based practice, role confusion, cost/efficiency, the history of practical nurse education and employer interests. The complex relationships between practical nursing and the disciplinary landscape of nursing are revealed in the analysis of discourses related to the purpose(s) of practical nurse education, curricula/educational programming, relationships between RN and PN education and the role of nursing knowledge. Power dynamics related to employer needs and interests, as well as educational silos and the nature of women's work, are also revealed within the intersection of various discourses.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.643
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.119
GPT teacher head0.596
Teacher spread0.477 · 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.

Study designSystematic review
Domainnot available
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

Citations20
Published2014
Admission routes2
Has abstractyes

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