Linking Public Safety And Public Health Data For Firearm Suicide Prevention In Utah
Bibliographic record
Abstract
In Utah, a state with a high rate of gun ownership, the shared concerns of diverse stakeholders generated bipartisan support for a state-funded study that tracked patterns of firearm suicide. The study linked sensitive public health and public safety data and identified opportunities for firearm suicide prevention. Findings reported to the state legislature included the proportion of suicide decedents who could have passed a background check for legal firearm possession at their time of death, had a permit to carry a concealed firearm, or had been seen in the hospital for a previous suicide attempt or self-harm. Within six months of the report's release, the legislature, health care and religious groups, and state agencies had launched diverse, major initiatives to reduce firearm suicide that were informed by the report's findings. We present the Utah experience as a case study in bringing diverse stakeholders-particularly gun owners-together to find common ground on firearm suicide prevention and in using linked data to support and guide their efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".