The Impact of Heavy Perceived Nurse Workloads on Patient and Nurse Outcomes
Bibliographic record
Abstract
This study investigated the relationships between seven workload factors and patient and nurse outcomes. (1) Background: Health systems researchers are beginning to address nurses’ workload demands at different unit, job and task levels; and the types of administrative interventions needed for specific workload demands. (2) Methods: This was a cross-sectional correlational study of 472 acute care nurses from British Columbia, Canada. The workload factors included nurse reports of unit-level RN staffing levels and patient acuity and patient dependency; job-level nurse perceptions of heavy workloads, nursing tasks left undone and compromised standards; and task-level interruptions to work flow. Patient outcomes were nurse-reported frequencies of medication errors, patient falls and urinary tract infections; and nurse outcomes were emotional exhaustion and job satisfaction. (3) Results: Job-level perceptions of heavy workloads and task-level interruptions had significant direct effects on patient and nurse outcomes. Tasks left undone mediated the relationships between heavy workloads and nurse and patient outcomes; and between interruptions and nurse and patient outcomes. Compromised professional nursing standards mediated the relationships between heavy workloads and nurse outcomes; and between interruptions and nurse outcomes. (4) Conclusion: Administrators should work collaboratively with nurses to identify work environment strategies that ameliorate workload demands at different levels.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".