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Record W2069176408 · doi:10.1108/13620430610683070

Workaholism, organizational life and well‐being of Norwegian nursing staff

2006· article· en· W2069176408 on OpenAlexaff
Ronald J. Burke, Stig Berge Matthiesen, Ståle Pallesen

Bibliographic record

VenueCareer Development International · 2006
Typearticle
Languageen
FieldPsychology
TopicWorkaholism, burnout, and well-being
Canadian institutionsYork University
Fundersnot available
KeywordsGeneralizability theoryNorwegianPsychologyPersonalityJob satisfactionBurnoutBig Five personality traitsMultilevel modelNursingApplied psychologySocial psychologyClinical psychologyMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to examine the relationship of individual difference personality characteristics (Big Five, generalized self‐efficacy), workaholism components and work life factors on measures of job satisfaction, burnout and health complaints. Design/methodology/approach Data were gathered from 496 nursing staff caring for terminally ill patients in five health care facilities in Norway using questionnaires. Findings Hierarchical regression analyses, controlling for personal demographic and work setting characteristics, indicated strong relationships of particular Big Five personality factors, workaholism components and work life factors with both job satisfaction and burnout; health complaints were only predicted by personality factors. Practical implications Future research must examine the generalizability of these findings to other samples in different countries. Implications for management and organizations are offered. Originality/value This paper contributes to the understanding of personality factors to workaholics in work outcomes and well‐being.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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

Citations64
Published2006
Admission routes1
Has abstractyes

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