Workplace incivility and organizational outcomes: Is the quality of social relationships at work important for employee attitudes, behaviours, and mental health?
Bibliographic record
Abstract
Workplace incivility is a relatively recently explored social phenomenon. The current study aimed to support previous findings indicating that workplace incivility is negatively related to employee and organizational well-being. This study expanded the evaluation of workplace incivility to include supervisor, coworker, and instigated incivility as separate constructs of workplace incivility. Nurses in Ontario and Nova Scotia completed questionnaires assessing their perceptions of workplace incivility, job satisfaction, turnover intentions, mental health, and manageable workload. Factor analysis results indicated that supervisor, coworker, and instigated incivility were indeed separate constructs. Hierarchical multiple regressions confirmed that supervisor and coworker incivility were significant predictors of job satisfaction, turnover intentions, employee mental health symptoms, and instigated incivility. Furthermore, job satisfaction mediated the relationship between workplace incivility and turnover intentions. The findings have implications for research and interventions to improve employee communication and develop an organizational culture with clear norms of civility and respect in the workplace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".