Neighborhood Differences in Omnipresent Policing and Sleep Health in New York City: Protocol for a Multimethod Quantitative Study
Bibliographic record
Abstract
BACKGROUND: Poor or insufficient sleep is associated with numerous adverse, potentially serious physical and mental health outcomes. Equally concerning are the substantial racial, gender, and socioeconomic disparities, with minorities and those experiencing poverty disproportionately affected by poor sleep quality and sleep disorders. Both theory and research suggest that sleep health is negatively impacted by concentrated poverty at the neighborhood level due to the deterioration of the built and social environments, thereby creating conditions that disrupt sleep. OBJECTIVE: This research considers an under-studied factor related to these conditions and sleep health-policing and police surveillance. Specifically, the study compares 4 neighborhoods within New York City at different levels of residential segregation. METHODS: The study design consists of a baseline survey, with 40 residents recruited in each neighborhood, and a 1-week diary phase with a subsample of residents. Neighborhood conditions are also assessed in each of the neighborhoods using a neighborhood audit tool. RESULTS: The study received funding in July 2024. Data collection commenced in September 2024. As of August 2025, we have enrolled more than 100 participants in the baseline survey. Planned analyses will begin once data collection has concluded. CONCLUSIONS: This information will help establish the extent to which surveillance and policing differentially impact the lives of New Yorkers as a function of where they live. Specifically, the results should be relevant and important for understanding the impact of novel policing strategies on underprivileged neighborhoods. This exploratory research will be useful for identifying populations and residential settings that may be most at risk for poor sleep health. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/82605.
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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 | 0.038 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.081 | 0.011 |
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".