Effect of Nursing Care Delivery Models on Registered Nurse Outcomes
Bibliographic record
Abstract
The two key components of models of nursing care delivery are mode of nursing care delivery and skill mix. While mode of nursing care delivery refers to the independent or collaborative work of nurses to provide care to a group of patients, skill mix is defined as direct care nurse classifications. Previous research has typically focused on only one component at a time (mode or skill mix). There exists little research that investigates both components simultaneously. This study examined the effect of mode of nursing care delivery and skill mix on nurse emotional exhaustion and job satisfaction after controlling for nurse demographics, workload factors, and work environment factors. A secondary analysis was done with survey data from 416 British Columbia medical-surgical registered nurses. Data were analyzed using hierarchical multiple regression and moderated regression. Registered nurses in a skill mix with licensed practical nurses reported lower emotional exhaustion when caring for more acute patients compared with those in a skill mix without licensed practical nurses. While mode of nursing care delivery was not related to nurse outcomes, work environment factors were the strongest predictors of both nurse outcomes. Skill mix moderated the relationship between patient acuity and emotional exhaustion. Nurse managers should invest in nurses' conditions of work environments.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".