Factors related to on-the-job abuse of nurses by patients.
Bibliographic record
Abstract
BACKGROUND: Numerous studies indicate that health care providers, particularly nurses, face a high risk of on-the-job abuse from patients. This article examines physical and emotional abuse from patients in nurses working in hospitals or long-term care facilities. DATA AND METHODS: Data are from the 2005 National Survey of the Work and Health of Nurses. Cross-tabulations were used to examine abuse in relation to personal characteristics of the nurse, job characteristics, and workplace climate factors. Multiple logistic regression modeling was used to examine abuse in relation to staffing and resource adequacy and relations among colleagues, controlling for personal and job characteristics. RESULTS: In 2005, 34% of Canadian nurses providing direct care in hospitals or long-term care facilities reported physical assault by a patient in the previous year; 47% reported emotional abuse. Abuse was related to being male, having less experience, usually working non-day shifts, and perceiving staffing or resources as inadequate, nurse-physician relations as poor, and co-worker and supervisor support as low. Associations between abuse and staffing or resource inadequacy and poor working relations persisted when controlling for personal and job characteristics. INTERPRETATION: Modifiable factors are important to nurses' on-the-job safety.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".