Nurses’ work environments, care rationing, job outcomes, and quality of care on neonatal units
Bibliographic record
Abstract
AIM: This paper is a report of a study of the relationship between work environment characteristics and neonatal intensive care unit nurses' perceptions of care rationing, job outcomes, and quality of care. BACKGROUND: International evidence suggests that attention to work environments might improve nurse recruitment and retention, and the quality of care. However, comparatively little attention has been given to neonatal care, a specialty where patient and nurse outcomes are potentially quite sensitive to problems with staffing and work environments. METHODS: Over a 6-month period in 2007-2008, a questionnaire containing measures of work environment characteristics, nursing care rationing, job satisfaction, burnout and quality of care was distributed to 553 nurses in all neonatal intensive care units in the province of Quebec (Canada). RESULTS: A total of 339 nurses (61.3%) completed questionnaires. Overall, 18.6% were dissatisfied with their job, 35.7% showed high emotional exhaustion, and 19.2% rated the quality of care on their unit as fair or poor. Care activities most frequently rationed because of insufficient time were discharge planning, parental support and teaching, and comfort care. In multivariate analyses, higher work environment ratings were related to lower likelihood of reporting rationing and burnout, and better ratings of quality of care and job satisfaction. CONCLUSION: Additional research on the determinants of nurse outcomes, the quality of patient care, and the impact of rationing of nursing care on patient outcomes in neonatal intensive care units is required. The Neonatal Extent of Work Rationing Instrument appears to be a useful tool for monitoring the extent of rationing of nursing care in neonatal units.
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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 | 0.005 |
| 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 | 0.000 |
| 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".