Operating room nurses' perceptions of the effects of physician‐perpetrated abuse
Bibliographic record
Abstract
BACKGROUND: Operating room (OR) nurses experience abuse perpetrated by physicians; however, little research has been conducted to examine nurses' perceptions of the effects of such abuse. AIMS: The aim of this research was to understand participants' perceptions of physician-perpetrated abuse on their health and ability to provide patient care. MATERIALS/METHODS: In this qualitative descriptive study, ten operating room nurses working in Eastern Canada participated in open-ended, individual audiotaped interviews that were transcribed for analysis using Boyatzis' method for code development. RESULTS: Three categories of factors contributing to abuse were developed. The first, culture of the OR, included environment and hierarchy. The second, catalysts of abuse, included nurses' positions and experience as well as non-nurse factors such as resources and interpersonal relationships among physicians. The third category, perceived effects, included psychological, physical and social health consequences for nurses. Effects on patient care consisted of safety and potential challenges to access. DISCUSSION: Nursing practice implications included mentoring, support and accountability for action. Educational implications related to interdisciplinary education and increased education on communication, assertiveness, and awareness of abuse. Implications for research included studying perceptions of other health-care providers including physicians, studying recruitment and retention in relation to abuse, and studying other abuse in health care such as horizontal violence. CONCLUSION: We suggest a proactive approach for empowering OR nurses to address abuse and an increased focus on interdisciplinary roles.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".