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The influence of leadership practices and empowerment on Canadian nurse manager outcomes

2011· article· en· W2103323099 on OpenAlexaffabout
Heather K. Spence Laschinger, Carol Wong, Ashley L. Grau, Lisa M. Pineau Stam

Bibliographic record

VenueJournal of Nursing Management · 2011
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsWestern University
Fundersnot available
KeywordsTransformational leadershipNursingEmpowermentMiddle managementPath analysis (statistics)Nursing managementPsychologyNurse managerNurse AdministratorOrganizational cultureAcute careQuality (philosophy)Health careMedicineMEDLINEPublic relationsSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

AIM: To examine the influence of senior nurse leadership practices on middle and first-line nurse managers' experiences of empowerment and organizational support and ultimately on their perceptions of patient care quality and turnover intentions. BACKGROUND: Empowering leadership has played an important role in staff nurse retention but there is limited research to explain the mechanisms by which leadership influences nurse managers' turnover intentions. METHODS: This study was a secondary analysis of data collected using non-experimental, predictive mailed survey design. Data from 231 middle and 788 first-line Canadian acute care managers was used to test the hypothesized model using path analysis in each group. RESULTS: The results showed an adequate fit of the hypothesized model in both groups but with an added path between leadership practices and support in the middle line group. CONCLUSIONS: Transformational leadership practices of senior nurses empower middle- and first-line nurse managers, leading to increased perceptions of organizational support, quality care and decreased intent to leave. IMPLICATIONS FOR NURSING MANAGEMENT: Empowered nurse managers at all levels who feel supported by their organizations are more likely to stay in their roles, remain committed to achieving quality patient care and act as influential role models for potential future leaders.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.500

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 head0.091
GPT teacher head0.352
Teacher spread0.260 · 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

Citations90
Published2011
Admission routes2
Has abstractyes

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