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Record W2121845282 · doi:10.12927/cjnl.2006.18368

Workplace Empowerment, Work Engagement and Organizational Commitment of New Graduate Nurses

2006· article· en· W2121845282 on OpenAlexaffvenue
Julia Cho, Heather Spence Laschinger, Carol Wong

Bibliographic record

VenueNursing leadership · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsMinistry of Health and Long Term Care
Fundersnot available
KeywordsWork engagementPsychologyEmpowermentWorkforceBurnoutOrganizational commitmentEmployee engagementNursingEmotional exhaustionTest (biology)Structural equation modelingSocial psychologyWork (physics)Public relationsClinical psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

As a large cohort of experienced nurses approaches retirement, it is critical to examine factors that will promote the engagement and empowerment of the newer workforce, allowing them to provide high quality patient care. The authors used a predictive, non-experimental survey design to test a theoretical model in a sample of new graduate nurses. More specifically, the relationships among structural empowerment, six areas of work life (conceptualized as antecedents of work engagement), emotional exhaustion and organizational commitment were examined. As predicted, structural empowerment had a direct positive effect on the areas of work life, which in turn had a direct negative effect on emotional exhaustion. Subsequently, emotional exhaustion had a direct negative effect on commitment. Implications of these findings for nursing administrators are discussed.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.127
GPT teacher head0.278
Teacher spread0.151 · 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

Citations313
Published2006
Admission routes2
Has abstractyes

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