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Understanding the mediating role of toxic emotional experiences in the relationship between negative emotions and adverse outcomes

2012· article· en· W1933875560 on OpenAlexafffund
Tina Kiefer, Laurie J. Barclay

Bibliographic record

VenueJournal of Occupational and Organizational Psychology · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsPsychologyNegative emotionSocial psychologyDifferential effectsDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Research has demonstrated that experiencing negative emotions at work can have adverse consequences for individuals and organizations. However, little research has explored why negative emotions can be associated with detrimental effects. To address this gap, the authors argue that it is critical to consider how emotions are experienced. Across two field studies ( N = 876 and N = 136), the authors investigate the mediating role of toxic emotional experiences (TEEs) in the relationship between negative emotions and adverse outcomes. The three TEEs dimensions (i.e., psychologically recurring, disconnecting, and draining) are examined as well as the composite score. Results indicated that the TEEs composite mediated the relationship between negative emotions and psychological health, attitudes towards the organization, performance, and helping behaviours. Mixed results were found for the three TEEs dimensions. A number of theoretical implications are explored including the differential roles of negative emotions versus TEEs, the distinct effects associated with each of the TEEs dimensions, and the importance of exploring how emotions are experienced at work. Practical implications related to effectively managing negative emotions and preventing ‘toxic' experiences are also 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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.122
GPT teacher head0.345
Teacher spread0.223 · 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 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

Citations47
Published2012
Admission routes2
Has abstractyes

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