The effects of confrontation and avoidance coping in response to workplace incivility.
Bibliographic record
Abstract
Workplace incivility has significant adverse consequences for targets. However, we know remarkably little about how targets of incivility cope and even less about which coping strategies are effective. Drawing on the coping process of the transactional model of stress, we examine confrontation as a form of problem-focused coping and avoidance as a form of emotion-focused coping in response to incivility. We examine the effects of these coping strategies on reoccurrence of incivility, incivility enacted by targets, psychological forgiveness, and emotional exhaustion. Focusing on the target's perspective of a series of uncivil interactions between a target and perpetrator, we conducted a 3-wave study of employees from various occupations. Employing the critical incident technique, participants reported on an incident of workplace incivility, and then answered a series of questions over 3 waves of data collection regarding their interactions with this perpetrator. Our findings suggest that confrontation and avoidance are ineffective in preventing reoccurrence of incivility. Avoidance can additionally lead to increased emotional exhaustion, target-enacted incivility, and lower psychological forgiveness. However, confrontation coping has promise with regards to eliciting positive outcomes such as psychological forgiveness that are beneficial to interpersonal workplace relationships. (PsycINFO Database Record
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".