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Record W7061502418

THE RELATIONSHIP AMONGST LEADER EMPOWERING BEHAVIOURS, WORKPLACE EMPOWERMENT, NURSE-PHYSICIAN COLLABORATION AND RESPECT IN ACUTE CARE NURSES

2008· article· en· W7061502418 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2008
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsnot available
Fundersnot available
KeywordsAcute careEmpowermentInclusion (mineral)Sample (material)PerceptionTest (biology)Structural equation modeling
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to test a theoretical model based on Kanter’s theory of workplace empowerment in a sample of Ontario acute care nurses. The relationships among four key variables were explored: leader empowering behaviours, workplace empowerment, nurse-physician collaboration and respect. A cross-sectional correlational survey design was used to obtain data from a random sample of 300 nurses. Overall, acute care nurses perceived both their leaders’ behaviours and their workplaces to be moderately empowering. Nurses felt that they had leaders who expressed confidence in their abilities and fostered their autonomy; however, their leaders did not promote inclusion in organizational decision-making. Nurses reported that the most empowering structure in their workplace was opportunity, and the least, formal power. Nurses perceived their work relationships with physicians to be moderately collaborative and their perceptions of respect overall as moderate. Overall, leader empowering behaviours had both a direct effect and an indirect effect on respect, through structural empowerment and nurse-physician collaboration. The final model revealed a reasonably good fit (x2 = 15.5, df=2, CFI=.92, IFI=.92, RMSEA=.21).

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)0.005
metaresearch head score (Gemma)0.030
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 score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.036
GPT teacher head0.300
Teacher spread0.264 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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