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RESONANT LEADERSHIP PRACTICES OF NURSE MANAGERS IN THE HOSPITAL SETTING: A CROSS-SECTIONAL STUDY

2022· article· en· W4312268522 on OpenAlexaff
Petrinella Onetia Fiana Reynolds, Bruna Moreno Dias, Cézar Augusto da Silva Flores, Alexandre Pazetto Balsanelli, Carmen Silvia Gabriel, Andréa Bernardes

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2022
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsGeorgetown Hospital
Fundersnot available
KeywordsLeadership styleNursingPerspective (graphical)Nurse AdministratorCross-sectional studyRating scalePsychologyScale (ratio)Nurse managerMedicineFamily medicineMEDLINEPolitical scienceSocial psychologyGeography

Abstract

fetched live from OpenAlex

ABSTRACT Objective: analyze Resonant Leadership style among the nurse managers from the perspective of nurse managers and nurses. Methods: cross-sectional study, carried out in a hospital in Guyana. Participants were 171 registered nurses and nurse managers. Data were collected from July to October 2020 using Resonant Leadership scale and a socio demographic questionnaire. Results: in self-version, the total leadership rating was 38.9 for nurses and 41.6 for managers (p=0.003). The age category at most 30 are more likely to give a low rating of themselves than the age category 50 - 69 (p = 0.046). Managers scored on average 3.44 points more than nurses for the self total leadership rating. Conclusion: resonant leadership is practiced at a moderate level and managers have higher scores. Nurse Managers can improve their leadership style.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.432
GPT teacher head0.503
Teacher spread0.070 · 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 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

Citations6
Published2022
Admission routes1
Has abstractyes

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