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Organizational Trust and Empowerment in Restructured Healthcare Settings

2000· article· en· W2018018956 on OpenAlexaffabout
Heather K. Spence Laschinger, Joan Finegan, Judith Shamian, Shelley Casier

Bibliographic record

VenueJONA The Journal of Nursing Administration · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsLondon Health Sciences CentreHealth CanadaWestern University
Fundersnot available
KeywordsOrganizational commitmentEmpowermentPsychologyHealth careAffective events theoryPerceived organizational supportOrganizational effectivenessJob satisfactionBusinessPublic relationsSocial psychologyJob performancePolitical scienceJob attitude

Abstract

fetched live from OpenAlex

In today's dramatically restructured healthcare work environments, organizational trust is an increasingly important element in determining employee performance and commitment to the organization. The authors used Kanter's model of workplace empowerment to examine the effects of organizational trust and empowerment on two types of organizational commitment. A predictive, nonexperimental design was used to test Kanter's theory in a random sample of 412 Canadian staff nurses. Empowered nurses reported higher levels of organizational trust, which in turn resulted in higher levels of affective commitment. However, empowerment did not predict continuance commitment--that is, commitment to stay in the organization based on perceived lack of other job opportunities. Because past research has linked affective commitment to employee productivity, these results suggest that fostering environments that enhance perceptions of empowerment and organizational trust will have positive effects on organizational members and increase organizational effectiveness.

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)0.018
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.004
Scholarly communication0.0020.001
Open science0.0000.003
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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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

Citations286
Published2000
Admission routes2
Has abstractyes

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