Mitigating or Magnifying the Harmful Influence of Workplace Aggression: An Integrative Review
Bibliographic record
Abstract
As a substantial amount of research has accumulated on the harmful consequences of workplace aggression for target employees, we believe it is now of particular importance to examine moderators that alleviate or amplify these harmful effects. We ask the following questions: For whom is workplace aggression more or less detrimental? Moreover, what can target employees and the organization do to mitigate the harmful effects of aggression? We propose to address these questions with an integrative review of empirical research on moderators of the harmful effects of workplace aggression on targets. In this review, we identify and illustrate five broad perspectives that existing research has primarily used to explain the moderating effects: resource-depletion, social-relational, appraisal, self-regulation, and social-influence perspectives. In addition, we identify a large number of moderators and synthesize them into three categories of individual moderators—trait-based, intrapersonal, and coping-based—and three categories of contextual moderators—collective, interpersonal, and job-based. We address research findings on each category of moderators organized around the theoretical perspectives. We conclude with a general discussion of an overarching summary, redundant and saturated findings, as well as research gaps and future directions.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".