Understanding the mediating role of toxic emotional experiences in the relationship between negative emotions and adverse outcomes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".