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Leadership Development of Hispanic Nurses

2004· article· en· W2312945775 on OpenAlexaff
Antonia M. Villarruel, Nilda Peragallo

Bibliographic record

VenueNursing Administration Quarterly · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsRegional Municipality of Niagara
Fundersnot available
KeywordsEthnic groupCultural competenceLeadership developmentCompetence (human resources)Health careHealth equityTransformational leadershipPsychologyNursingMedicinePolitical sciencePublic relationsPedagogySocial psychologyPublic health

Abstract

fetched live from OpenAlex

The underrepresentation of racial and ethnic minority nurses and other health professions has been linked to the continued disparities in health outcomes for these populations (Institute of Medicine. Unequal Treatment. Confronting Racial and Ethnic Disparities in Health Care. Washington, DC: The National Academies Press; 2002). Strategies to reduce health disparities, such as increasing the cultural competence of majority nurses, often depend on leaders of the same racial and ethnic groups to provide leadership in research, education, practice, and communities. However, little is known about the leadership pathways of racial and ethnic minority nurses. In this descriptive study, 22 Hispanic nurse leaders completed an open-ended survey related to their definitions of leadership, and challenges and opportunities in their leadership development. Although there are similarities in many aspects of leadership development, the lack of Hispanics in the workplace, the ability and responsibilities related to being bilingual and bicultural, and perceived discrimination in the work setting are additional challenges identified by Hispanic nurses. The importance of role models and mentors in facilitating leadership development is a dominant theme. Results of this study provide direction not only for the development of leadership for Hispanic nurses but also for other racial and ethnic minority nurses.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.210
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.111
GPT teacher head0.387
Teacher spread0.276 · 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.

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

Citations19
Published2004
Admission routes1
Has abstractyes

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