# Uncovering Intervention Opportunities for Suicide Prevention with Language Model Assistants

arXiv: https://arxiv.org/abs/2508.18541 · HTML: https://mlsanity.com/paper/2508.18541/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [Can LLMs Understand the Impact of Trauma? Costs and Benefits of LLMs Coding the Interviews of Firearm Violence Survivors](https://mlsanity.com/paper/2604.16132/) (2026)

## Abstract (arXiv)

Warning: This paper discusses topics of suicide and suicidal ideation, which may be distressing to some readers. The National Violent Death Reporting System (NVDRS) documents information about suicides in the United States, including free text narratives (e.g., circumstances surrounding a suicide). In a demanding public health data pipeline, annotators manually extract structured information from death investigation records following extensive guidelines developed painstakingly by experts. In this work, we facilitate data-driven insights from the NVDRS data to support the development of novel suicide interventions by investigating the value of language models (LMs) as efficient assistants to these (a) data annotators and (b) experts. We find that LM predictions match existing data annotations about 85% of the time across 50 NVDRS variables. In the cases where the LM disagrees with existing annotations, expert review reveals that LM assistants can surface annotation discrepancies 38% of the time. Finally, we introduce a human-in-the-loop algorithm to assist experts in efficiently building and refining guidelines for annotating new variables by allowing them to focus only on providing feedback for incorrect LM predictions. We apply our algorithm to a real-world case study for a new variable that characterizes victim interactions with lawyers and demonstrate that it achieves comparable annotation quality with a laborious manual approach. Our findings provide evidence that LMs can serve as effective assistants to public health researchers who handle sensitive data in high-stakes scenarios.