Workplace incivility in Japan: Reliability and validity of the Japanese version of the modified Work Incivility Scale
Bibliographic record
Abstract
OBJECTIVES: Although incivility is a common interpersonal mistreatment and associated with poor mental health, there are few studies about it in Asian countries. The aim of this study was to develop the Japanese version of the modified Work Incivility Scale (J-MWIS), investigate its reliability and validity, and reveal the prevalence of incivility among Japanese employees in comparison with data on Canadian employees. METHODS: A total of 2,191 Japanese and 1,071 Canadian employees were surveyed, using either the J-MWIS or MWIS. Japanese employees additionally answered questions on civility, worksite social support, workplace bullying, psychological distress, intention to leave, and work engagement to investigate construct validity. RESULTS: At least one form of workplace incivility was experienced by both Japanese (52.3%) and Canadian (86.0%) employees in the previous month. Internal consistency reliability of the J-MWIS was acceptable (α=0.71-0.81), and correlation analyses also confirmed its construct validity as expected. Workplace incivility was associated with lower workgroup civility, lower supervisor and coworker support, higher workplace bullying, higher psychological distress, higher intention to leave, and lower work engagement. Confirmatory factor analyses showed that the original three-factor model (supervisor incivility, coworker incivility, and instigated incivility) fitted moderately in both Japan and Canada data, though the privacy/overfamiliarity factor was additionally extracted from exploratory factor analysis for the J-MWIS. CONCLUSIONS: The results of this study suggested that the J-MWIS has moderate internal consistency reliability and good construct validity.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".