Early Results of an Initiative to Assess Exposure to Firearm Violence in Ambulatory Care: Descriptive Analysis of Electronic Health Record Data
Bibliographic record
Abstract
BACKGROUND: Current research on firearm violence is largely limited to patients who received care in emergency departments or inpatient acute care settings or who died. This is because standardized disease classification codes for firearm injury only represent bodily trauma. As a result, research on pathways and health impacts of firearm violence is largely limited to people who experienced acute bodily trauma and does not include the estimated millions of individuals who were exposed to firearm violence but did not sustain acute injury. Assessing and collecting data on exposure to firearm violence in ambulatory care settings can expand research and more fully frame the public health issue. OBJECTIVE: The aim of the study is to evaluate the demographic and clinical characteristics of patients who self-reported exposure to firearm violence during a behavioral health visit. METHODS: This study assessed early data from an initiative implemented in 2022 across a national network of ambulatory behavioral health centers to support trauma-informed care by integrating structured data fields on trauma exposure into an electronic health record behavioral health patient assessment form (SmartForm), as such variables are generally not included in standard outpatient medical records. We calculated descriptive statistics on clinic characteristics, patient demographics, and select clinical conditions among clinics that chose to implement the SmartForm and among patients who reported an exposure to firearm violence. Data on patient counts are limited to positive reports of exposure to firearm violence, and the representativeness of firearm exposure among all patients could not be calculated due to unknown variability in the implementation of the SmartForm. RESULTS: There were 323 of 629 (51%) clinics that implemented the SmartForm and reported at least 1 patient exposed to firearm violence. In the first 11 months of implementation, 3165 patients reported a recent or past exposure to firearm violence across the 323 clinics. Among patients reporting exposure, 52.7% (n=1669) were male, 38.8% (n=1229) were Black, 45.7% (n=1445) had posttraumatic stress disorder, 37.5% (n=1186) had a substance abuse disorder (other than nicotine), and 11.7% (n=371) had hypertension. CONCLUSIONS: Current research on firearm violence using standardized data is limited to acute care settings and death data. Early results from an initiative across a large network of behavioral health clinics demonstrate that a high number of clinics chose to implement the SmartForm, resulting in thousands of patients reporting exposure to firearm violence. This study demonstrates that collecting standardized data on firearm violence exposure in ambulatory care settings is feasible. This study further demonstrates that resultant data from ambulatory settings can be used for meaningful analysis in describing populations affected by firearm violence. The results of this study hold promise for further collection of structured data on exposure to firearm violence in ambulatory settings.
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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 | 0.030 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".