The impact of heavy nurse workload and patient/family complaints on workplace violence: An application of human factors framework
Bibliographic record
Abstract
Aim: To examine the relationships between workload factors at different systems levels (unit level, job level and task level), patients/family complaints and nurse reports of patient violence towards them using a human factors framework. Design: This is a secondary analysis of cross-sectional data. Methods: Data from 528 nurses working in medical-surgical settings in British Columbia, Canada, were analysed. At the unit-level workload factors included patient-RN ratios, patient acuity and dependency; at the job-level perceptions of heavy workload, undone nursing tasks and compromised professional standards due to workload; and at the task-level interruptions to workflow. Results: Workload factors at multiple levels were directly related to workplace violence. Nurses' increased reports of compromised standards (job level) and interruptions (task level) were related to increased reports of physical and emotional violence, and higher patient acuity (unit level) was related to increased reports of emotional violence. Patient/family complaints mediated the relationship between almost all the workload factors and workplace violence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".