Nursing Work Environment Staffing Councils
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the self-reported perceptions of the healthy work environment (HWE) of nurses who are members of Nursing Workplace Environment and Staffing Councils (NWESCs). BACKGROUND: In a statewide initiative, NWESCs were established at hospitals throughout the state of New Jersey as an alternative to nurse staffing ratio laws and to provide clinical nurses a voice in determining resources needed for patient care and support an HWE. METHODS: This quantitative descriptive study presents the results of the Healthy Workplace Environment Assessment Tool (HWEAT) and open-ended questions about NWESCs among a sample of 352 nurses. RESULTS: Three years after NWESC implementation, all HWEAT standard mean scores increased and were rated higher than the American Association of Critical-Care Nurses benchmark. There were statistically significant differences in clinical nurses' perceptions of an HWE compared with nurse leaders. Respondents also shared their NWESC's best practices and challenges. Responses to questions identified NWESC best practices and challenges. CONCLUSION: This study offers insight into the improvement in nurses' perceptions of the HWE after the introduction of a statewide NWESCs. Structures such as the NWESCs may provide an alternative to mandated staffing ratios.
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 | 0.003 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".