What’s in an eye roll? It is time we explore the role of workplace incivility in healthcare
Bibliographic record
Abstract
A recent study of patient violence toward hospital physicians and nurses offers a welcome perspective in its classifying of aggressive behaviors as workplace violence. While patients and families are widely recognized as sources of rude behaviors, we need to shed light on passive aggressive and other low-level rude behaviors that take place frequently amongst hospital personnel as well. Studied under the term "workplace incivility," these seemingly insignificant behaviors that show lack of regard for colleagues have far reaching negative consequences. Examples of such consequences include intentionally reducing work effort, spending time worrying, and taking frustration out on customers. In addition, incivility creates a spiral effect, where one type of incivility breeds other forms of same. In healthcare, rudeness plays a pivotal role due to its negative impact, which goes to the heart of service delivery. For example, healthcare professionals who are exposed to incivility, even when not directed specifically at them, are at risk of inflicting iatrogenic injury. Within the complexity of hospital environments, incivility gets fueled and maintained by underlying beliefs such as "because we work in a high-pressure environment, it's okay to skip the niceties." Tackling these beliefs is key to taming workplace incivility. This article poses questions worthy of further scientific inquiry. Finally, Israeli researchers and practitioners are advised to find a better term for workplace incivility to replace the currently used, excessively negative term gasut ruach.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".