Advancing Lifestyle Medicine in New York City’s Public Health Care System
Bibliographic record
Abstract
Chronic diseases are the leading cause of death and disability in the United States, and much of this burden can be attributed to lifestyle and behavioral risk factors. Lifestyle medicine is an approach to preventing and treating lifestyle-related chronic disease using evidence-based lifestyle modification as a primary modality. NYC Health + Hospitals, the largest municipal public health care system in the United States, is a national pioneer in incorporating lifestyle medicine systemwide. In 2019, a pilot lifestyle medicine program was launched at NYC Health + Hospitals/Bellevue to improve cardiometabolic health in high-risk patients through intensive support for evidence-based lifestyle changes. Analyses of program data collected from January 29, 2019 to February 26, 2020 demonstrated feasibility, high demand for services, high patient satisfaction, and clinically and statistically significant improvements in cardiometabolic risk factors. This pilot is being expanded to 6 new NYC Health + Hospitals sites spanning all 5 NYC boroughs. As part of the expansion, many changes have been implemented to enhance the original pilot model, scale services effectively, and generate more interest and incentives in lifestyle medicine for staff and patients across the health care system, including a plant-based default meal program for inpatients. This narrative review describes the pilot model and outcomes, the expansion process, and lessons learned to serve as a guide for other health systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".