A meta-analysis of experienced incivility and its correlates: Exploring the dual path model of experienced workplace incivility.
Bibliographic record
Abstract
The present study proposes and examines a theoretical Dual Path Model of Experienced Workplace Incivility using meta-analytic relationships (k = 246; N = 145, 008) between experienced incivility and frequent correlates. The stress-induced mechanism was supported with perceived stress mediating the meta-analytical relationship between experienced incivility and occupational health (i.e., emotional exhaustion and somatic complaints). The commitment-induced mechanism was also supported with affective commitment to the organization mediating the relationship between experienced incivility and organizational correlates (i.e., job satisfaction and turnover intentions). However, these paths were not able to explain the strong relationship between experienced and enacted workplace incivility. Moderating analysis revealed that the experienced-enactment link is stronger between coworkers, in comparison to incivility experienced from supervisors; experienced incivility is more strongly related to organizational correlates, when incivility is enacted by supervisors in comparison to coworkers, and in human service samples when compared to samples comprised of mixed occupations. We discuss theoretical and practical implications as well as directions for future research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".