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Record W2888343915 · doi:10.31372/20180302.1082

Leadership in Nursing in Korea

2018· article· en· W2888343915 on OpenAlexvenueno aff
Myungsun Yi

Bibliographic record

VenueAsian/Pacific Island Nursing Journal · 2018
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsNursingLife expectancyWorkforceHealth careCourageMedicinePsychologyPolitical sciencePopulation

Abstract

fetched live from OpenAlex

Nurses are the largest workforce that impacts the health of the people. As the life expectancy and the number of people with chronic diseases increases in Asia, it is important for Asian nursing professionals to play a pivotal role in improving health status and quality of life by demonstrating their leadership. This article focuses on nursing leadership in Korea. Leadership exemplars were described in detail to show how successful nursing leaders set goals and challenged to meet these goals, despite numerous obstacles that exist in male-oriented society of Korea. And three essential elements that future nurse leaders should consider were suggested. First, awareness was emphasized as the first step to becoming a leader. Clear awareness about women and nursing would help nursing leaders reduce or eliminate gender biases that can undermine nurses' achievements and limit their advancement. Second, challenge was stressed as nursing leaders must challenge and expand the horizon of nursing by facing their situations and problems with courage. Lastly, transformation was highlighted, as the ultimate role of a leader is to make the world better. Four leadership frames-structural frame, human resource frame, political frame, and symbolic frame-were introduced to help guide their leadership journey to change the world. The results of this study would help nurses innovate with excellent leadership by efficiently managing nursing resources and by helping nursing organizations to adjust to changing demands in health care.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.054
GPT teacher head0.328
Teacher spread0.274 · 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 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

Citations5
Published2018
Admission routes1
Has abstractyes

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