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Record W2162286690 · doi:10.1177/0193945914521304

Spirit at Work (SAW)

2014· article· en· W2162286690 on OpenAlexaffabout
Joan Wagner, David Gregory

Bibliographic record

VenueWestern Journal of Nursing Research · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsLISRELJob satisfactionPsychologyOrganizational commitmentNursingHealth carePerceptionApplied psychologySocial psychologyStructural equation modelingMedicineComputer science

Abstract

fetched live from OpenAlex

A cross-sectional mixed-method survey explored and measured relationships between spirit at work (SAW) concepts, experience, education, practice context, job satisfaction, and organizational commitment using LISREL 8.80 and 2012 survey data from a random sample of 217 surgical and 158 home care registered nurses (RNs) in western Canada. Qualitative data underwent content analysis using a priori coding categories based on established theory. Final model indices fit the observed data. SAW concepts of engaging work and mystical experience accounted for moderate to large amounts of model variance for both home care and surgical nurses, while significant positive relationships between SAW concepts, job satisfaction, and organizational commitment were also reported. Researchers concluded that SAW contributes to improved job satisfaction and organizational commitment while being sensitive to RN experiences across clinical contexts. As an holistic measure of RN workplace perceptions, SAW contributes essential information directed at creating optimal environments for both health care providers and recipients.

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)0.012
metaresearch head score (Gemma)0.001
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 score0.712
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.296
GPT teacher head0.502
Teacher spread0.206 · 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

Citations22
Published2014
Admission routes2
Has abstractyes

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