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Record W2800049733 · doi:10.1002/nop2.150

Describing the leadership capabilities of advanced practice nurses using a qualitative descriptive study

2018· article· en· W2800049733 on OpenAlexafffundabout
Alyson Lamb, Ruth Martin‐Misener, Denise Bryant‐Lukosius, Margot Latimer

Bibliographic record

VenueNursing Open · 2018
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsMcMaster UniversityDalhousie UniversityIzaak Walton Killam Health Centre
FundersIWK Health CentreDalhousie UniversityNova Scotia Health Research Foundation
KeywordsNonprobability samplingQualitative researchHealth careNursingContent analysisMedical educationPerceptionLeadership studiesLeadership developmentPsychologyMedicineLeadership styleSociologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

AIM: The aim of this study is to explore advanced practice nurses' perceptions of their leadership capabilities. DESIGN: A qualitative descriptive methodology informed by a well-established leadership framework was used to explore advanced practice nurses' perceptions of their leadership. METHODS: Purposive sampling of advanced practice nurses working in tertiary acute care facilities in Eastern Canada was employed. Data sources included face-to-face interviews and document analysis. Fourteen advanced practice nurses participated in two audio-taped semi-structured interviews from March 2013-January 2014. Data were transcribed and analysed using NVIVO 10 software and content analysis. RESULTS: Two main themes were identified: "Patient-focused leadership" and "organization and system-focused leadership". These two themes are further described through leadership domains and capabilities that clearly articulate advanced practice nursing leadership and its contribution to improving the care environment for patients and families, nurses and other healthcare providers, organizations and the healthcare system.

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)0.015
metaresearch head score (Gemma)0.017
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.005
Scholarly communication0.0040.003
Open science0.0010.003
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 head0.609
GPT teacher head0.584
Teacher spread0.025 · 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

Citations103
Published2018
Admission routes3
Has abstractyes

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