Nurse Reports From the Frontlines: Analysis of a Statewide Nurse Survey
Bibliographic record
Abstract
BACKGROUND: Registered nurses on the frontlines of care are increasingly burdened by changes in staffing, increased turnover, demands on their time and the continual need for advanced knowledge and training. We identify employment and environmental characteristics that may ultimately affect the quality of care METHODS: Surveys were mailed to a random sample of all registered nurses licensed and residing in large southeastern US State. Responses from 10, 951 nurses providing direct patient care were compared to national findings. Descriptive statistics were used to examine demographics, the practice environment, nurse outcomes and the quality of care. RESULTS: Nurses in this state are more racially diverse and less educated when compared to nurses nationally. Theses nurses report high levels of burnout and job dissatisfaction, and almost one-quarter intend to leave their jobs within the next year. The majority of nurses report good working relationships with physicians, but perceive problems with workplace management. CONCLUSION: Nurses report inadequate resources and the administrative support necessary to provide quality care. The proportion of nurses with baccalaureate and graduate education qualifications is less than is needed now and certainly insufficient for the future. Policy efforts must address these issues to retain our nurse workforce and improve the quality of patient care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".