Understanding workplace incivility dynamics from the perspective of conflict mediators: a qualitative study
Bibliographic record
Abstract
Background The research exploring workplace incivility has predominantly employed cross-sectional surveys, neglecting the dynamic aspect of the theory and failing to consider potential avenues for how the situation might be resolved. Objective To examine how uncivil interactions unfold over time, assess the impact of mediation on the resolution process, and evaluate how these interactions align with the complete theory of workplace incivility. Methods Descriptive phenomenology was used to analyze interviews with nine mediators (five women and four men) who explored the parties' experiences of incivility with each other and facilitated discussions with them to resolve their conflict. Findings A common structure emerged across the diverse situations described by mediators: personal norms shaped whether individuals attributed negative intentions and interpreted the other person's behavior negatively, which in turn triggered negative emotions and prompted a range of reactive responses (e.g., avoidance, confiding in others). During mediation, perspective taking played a key role in restoring trust, particularly when one party expressed vulnerability, which, in turn, facilitated reconciliation between the individuals involved. In contrast, in the two cases where individuals failed to engage in perspective taking reconciliation did not occur. Conclusion Future research should investigate the role of personal norms in shaping how individuals interpret others' behavior. Furthermore, additional investigations are needed to examine how perspective taking influences the progression of workplace incivility, using a dynamic lens to capture the complexity and evolving nature of these interactions.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".