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Record W2890415999 · doi:10.1515/ijnes-2017-0039

Student Preparation for Nursing Leadership: Lessons from an Undergraduate Programs Review

2018· article· en· W2890415999 on OpenAlexaff
Carolyn Ross, Joanne Olson, Kaysi Eastlick Kushner, Sadaf Saleem Murad, Wing Sze Wence Leung, Sara Daniels, Madge Applin, Tristan Eaton

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2018
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsAlberta Health ServicesAlberta Advanced EducationRed Deer PolytechnicKeyano CollegeAlberta Hospital EdmontonUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsDelegationContent analysisMedical educationNursingPsychologyQualitative researchMedicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

Facilitating student achievement of nationally recognized entry-level-competencies in nursing leadership is a critical step in building capacity to promote patient safety, quality of care, and healthy work environments. Data for this substudy were drawn from a larger multi-phased, multi-method, cross-sectional, descriptive study conducted to inform comprehensive undergraduate curricular revision. The research question for this substudy was: what are the perceptions about undergraduate program preparation for nursing leadership? Frequencies and percentages summarized relevant quantitative data from the surveys and deductive content analysis was used to further explore the category of leadership which emerged from content analysis of qualitative data in the larger study. Key findings illustrate need for additional attention to learning experiences in conflict management, delegation and supervision of clinical teams, and advocacy. Greater collaboration between educational and clinical agencies is needed to find mutually beneficial strategies to support nursing leadership development for nursing students and new graduates.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models agreeAgreement compares identical category sets and study designs across arms.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score1.000

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 communication0.0010.003
Open science0.0010.000
Research integrity0.0000.001
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 head0.211
GPT teacher head0.514
Teacher spread0.303 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2018
Admission routes1
Has abstractyes

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