Effect of Workplace Violence and Psychological Stress Responses on Medical-Surgical Nurses’ Medication Intake
Bibliographic record
Abstract
BACKGROUND: Workplace violence is a prevalent phenomenon in the health-care sector globally, but few studies have examined its impact on nurses' use of prescribed and/or over-the-counter medications to manage signs and symptoms. PURPOSE: The purpose of this study was to examine the direct and indirect effect of workplace violence, through the pathway of psychological stress responses, on nurses' frequencies of medication intake. An occupational stress and health outcomes model was tested in this study. METHODS: A secondary analysis of cross-sectional survey data from 551 medical-surgical nurses in British Columbia was conducted. Both emotional and physical workplace violence were examined. Emotional exhaustion and posttraumatic stress disorder were psychological stress responses to workplace violence. RESULTS: Emotional and physical violence from patients and/or families were the most prevalent sources of workplace violence. Physical violence and psychological stress responses increased the frequency of medication intake after controlling for nurse characteristics. Emotional violence was not related to medication intake over and above the effect of psychological stress responses. Physical and emotional violence elicited psychological stress responses resulting in increased medication use. CONCLUSION: Workplace violence triggers psychological stress responses with adverse outcomes on nurses' health and well-being.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".