Illinois community health needs assessments: Disparities in prioritizing firearm violence prevention
Bibliographic record
Abstract
BACKGROUND: Violent injuries contribute to significant death and disability in the United States every year. The number of hospitals who identify violence prevention as a priority is unknown. We sought to determine if hospitals in counties with high rates of firearm-related mortality identified violence prevention as a priority in their Community Health Needs Assessments (CHNAs) and if trauma center designation influenced this prioritization. METHODS: This was a cross-sectional review of all publicly available CHNAs for hospitals within 27 Illinois counties from 2021 to 2023. Firearm to all-mechanism mortality ratios from 2017 to 2021 were identified using age-adjusted 5-year mortality rates obtained from the CDC's WISQARS' Health Equity Data for Illinois and stratified into quartiles. Descriptive statistics were used to compare the prioritization of violence prevention by firearm to all-mechanism mortality ratios (highest vs. lowest quartile) and trauma designation. RESULTS: Of 93 hospitals in 27 counties, 52 (55.9%) identified violence as a community issue. Hospitals in counties with the highest firearm to all-mechanism mortality ratios were more likely to identify violence as a priority than the lowest quartile counties (62.7% vs. 38.5%, p = 0.03). Trauma centers were not more likely than nontrauma centers to prioritize violence in both high ( p = 0.29) and low ( p = 0.77) firearm mortality counties. Among hospitals identifying violence as a community health issue in their CHNA, only 25 (48.1%) outlined a plan to address it. CONCLUSION: While hospitals within counties in the highest quartile of firearm mortality were more likely to identify violence as a community issue, few outlined a plan to address it. In addition, trauma centers were not more likely to identify violence as a community issue. Enhanced support for evidence-based programs, such as hospital-based violence intervention programs, may help bridge the gap between identification and intervention. LEVEL OF EVIDENCE: Prognostic and Epidemiologic; Level IV.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".