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Record W4361290682 · doi:10.5430/jha.v12n1p9

Perceptions of leadership style between nurse managers and their staff in Eastern Saudi Arabia: A cross sectional survey

2023· article· en· W4361290682 on OpenAlexvenueno aff
Nourah Alsadaan, Amanda Kimpton, Linda Jones, Cliff DaCosta

Bibliographic record

VenueJournal of Hospital Administration · 2023
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
FundersRMIT University
KeywordsTransformational leadershipTransactional leadershipLeadership stylePerceptionWorkforceNursingPsychologyNurse AdministratorStyle (visual arts)Management stylesMedicinePublic relationsMEDLINESocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Background: Understanding nurses’ perceptions about their nurse managers is a crucial element to consider as it helps in the performance of the nurse managers and retention of nurses and reflects the nature of a competent workforce in achieving the organisational goals.Objective: To explore if there is a difference in perceptions of leadership style between nurse managers and their staff and discuss why this occurs.Methods: A cross-sectional descriptive comparative research design was used.Results: Nurse managers rated themselves as using transformational and transactional factors more than the nurses perceived them utilising these various leadership styles. Nurse managers, however, rated themselves lower than nurses in both laissez-faire and management-by-exception-passive.Discussion: The leadership style preferred by the followers is consistently rated higher than the leadership style that their leaders are utilising. Formation of accurate self-perception is a delicate process, especially for people in management positions. Bias in higher self-ratings may occur for several reasons, including gender, which forms the basis of this discussion.Conclusions: The results highlight the need for nurse managers to reflect on their practices and find new ways to enhance their leadership styles.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.081
GPT teacher head0.431
Teacher spread0.350 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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