Decreasing Incivility and Bullying Through the Development of a Healthy, Respectful Work Culture
Bibliographic record
Abstract
Abstract Problem: Incidents of bullying and incivility, including verbal aggression and exclusionary practices within the Telemetry department. Context: This quality improvement project addresses workplace incivility, bullying, and harassment. The project aims to develop and implement a Compact Agreement of Unit Norms to foster a healthier work environment. Interventions: Interventions included leadership education on bullying and conflict resolution, team awareness was heightened through workshops defining bullying and incivility, and a collaborative Compact Agreement of Unit Norms was developed and implemented. Measures: An analysis of reported incidents for the first quarter of 2024 through the Electronic Reposting System (ERRF) on the Telemetry floor revealed over 50 incidents of uncivil or bullying behaviors, including verbal aggression and exclusionary practices. Results: By June 2024, a significant improvement was observed with a total of 8 ERRFs, indicating a reduction of over 30% compared to the first quarter of 2024. Conclusions: The implications for practice based on this project highlight that reducing workplace incivility and bullying through the implementation of a Compact Agreement of Unit Norms fosters a healthier and more supportive work environment (Namie & Namie,2009). Keywords: Workplace incivility, bullying, harassment, Compact Agreement of Unit Norms.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".