Nursing Leadership at Nation's Leading Public Health System Addressing Health Equity and Social Determinants of Health at the Administrative Level and at the Bedside
Bibliographic record
Abstract
New York City Health + Hospitals (NYC H + H) is the largest public health care system in the United States, safeguarding 1.4 million patients annually, caring for 1 in every 6 New Yorkers through 11 essential hospitals, 5 post-acute care facilities, more than 70 community centers, and correctional health services in city jails. The 9600+ nurses and 970+ social workers represent the largest segment of the system's 40 000 employees, charged with delivering essential health care services to the most vulnerable and disadvantaged members of society, regardless of ethnicity, culture, creed, gender, age, sexual orientation, income, immigration, or insurance status. NYC H + H is in the process of reinventing nursing culture with a renewed focus on achieving true nursing excellence, emphasizing professional evidence-based best practices and a compassionate care delivery model, putting nurses in the forefront of all efforts to address the social determinants of health and the devastating consequences of health disparities. Systemwide implementation of foundational transformation is positioning nursing in the vanguard of the system's commitment to equity and diversity in the workplace, recognizing unconscious bias, calling out bigotry, and rooting out systemic racism, all key recommendations in the National Academies, The Future of Nursing 2020-2030: Charting a Path to Achieve Health Equity.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".