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Staff Nurse Empowerment in Line and Staff Organizational Structures for Chief Nurse Executives

2006· article· en· W2036568340 on OpenAlexaffabout
Sue Matthews, Heather K. Spence Laschinger, Lynne P. Johnstone

Bibliographic record

VenueJONA The Journal of Nursing Administration · 2006
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMinistry of Health and Long Term CareWestern University
Fundersnot available
KeywordsEmpowermentNursingOrganizational structurePsychologyLine managementNurse AdministratorOrganizational cultureHuman resourcesMedicineMEDLINEPublic relationsManagementPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: The authors tested a model linking chief nurse executive (CNE) organizational structures (line and staff) to staff nurse perceptions of workplace empowerment in 2 large Canadian hospitals. BACKGROUND: Kanter's theoretical constructs of empowerment (ie, access to information, support, resources and opportunity, and formal and informal power) were used to explore this phenomena. No published studies were found linking organizational structure to staff nurse empowerment. METHODS: Staff nurses (n = 256) were surveyed in 2 large teaching hospitals, one with a CNE in a line structure, the other with a CNE in a staff structure. Multiple regression analysis was used to test the proposed model. RESULTS: Staff nurses with a CNE in a line structure felt significantly more empowered in their access to resources than nurses with a CNE in a staff structure. Kanter's empowerment structures explained 63% of the variance in nurses' global empowerment in a line structure and 42% in the staff structure. Access to information, resources, and formal power was an important predictor of nurses' global empowerment in the line hospital, whereas only access to support was a significant predictor in the staff hospital. CONCLUSION: Support for the model tested in this study highlights the importance of the CNE in creating and sustaining healthy work environments for 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.543

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 head0.014
GPT teacher head0.323
Teacher spread0.308 · 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 designNot applicable
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
Published2006
Admission routes2
Has abstractyes

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