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Record W2070288047 · doi:10.1080/02678373.2013.782158

Workplace bullying and psychological health at work: The mediating role of satisfaction of needs for autonomy, competence and relatedness

2013· article· en· W2070288047 on OpenAlexaffabout
Sarah‐Geneviève Trépanier, Claude Fernet, Stéphanie Austin

Bibliographic record

VenueWork & Stress · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité du Québec à Montréal
Fundersnot available
KeywordsWorkplace bullyingAutonomyPsychologyWork engagementCompetence (human resources)BurnoutJob satisfactionSocial psychologySelf-determination theoryScholarshipOccupational safety and healthOccupational burnoutMental healthApplied psychologyClinical psychologyWork (physics)Emotional exhaustionMedicine

Abstract

fetched live from OpenAlex

The aim of this study was to investigate how exposure to workplace bullying undermines psychological health at work. Drawing on self-determination theory, this study proposes and tests a model in which the experience of workplace bullying predicts poor psychological health at work (higher burnout and lower work engagement) through lack of satisfaction of basic psychological needs (autonomy, competence and relatedness). The results of this study, conducted among 1179 nurses in Quebec, Canada, provide support for the model. Workplace bullying negatively predicted work engagement through employees' unsatisfied needs for autonomy, competence and relatedness. Workplace bullying also positively predicted burnout, via lack of satisfaction of employees' need for autonomy. Invariance analysis also confirmed the robustness of the model across gender and job status. Implications for workplace bullying research and managerial practices 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)0.001
metaresearch head score (Gemma)0.004
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 score0.136
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.302
Teacher spread0.277 · 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

Citations212
Published2013
Admission routes2
Has abstractyes

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