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Abstract ObjectivesSuicide presents a major public health challenge worldwide, affecting people across the lifespan. While previous studies revealed strong associations between Social Determinants of Health (SDoH) and suicide deaths, existing evidence is limited by the reliance on structured data. To resolve this, we aim to adapt a suicide-specific SDoH ontology (Suicide-SDoHO) and use natural language processing (NLP) to effectively identify individual-level SDoH-related social risks from death investigation narratives. Materials and MethodsWe used the latest National Violent Death Report System (NVDRS), which contains 267 804 victim suicide data from 2003 to 2019. After adapting the Suicide-SDoHO, we developed a transformer-based model to identify SDoH-related circumstances and crises in death investigation narratives. We applied our model retrospectively to annotate narratives whose crisis variables were not coded in NVDRS. The crisis rates were calculated as the percentage of the group’s total suicide population with the crisis present. ResultsThe Suicide-SDoHO contains 57 fine-grained circumstances in a hierarchical structure. Our classifier achieves AUCs of 0.966 and 0.942 for classifying circumstances and crises, respectively. Through the crisis trend analysis, we observed that not everyone is equally affected by SDoH-related social risks. For the economic stability crisis, our result showed a significant increase in crisis rate in 2007–2009, parallel with the Great Recession. ConclusionsThis is the first study curating a Suicide-SDoHO using death investigation narratives. We showcased that our model can effectively classify SDoH-related social risks through NLP approaches. We hope our study will facilitate the understanding of suicide crises and inform effective prevention strategies.
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This content will become publicly available on April 1, 2026
Narratives and destigmatization: the case of criminal record stigma in the labor market
Abstract Sociologists use the concept of narrative as an analytical tool and theoretical concept to understand the stories that people tell and their role in social and cultural life. A key tenet of prior research on narratives is their capacity to shape the audience’s understanding and evaluation of the narrator. In this mixed-method study, we investigate the role of narratives in destigmatization through the case of criminal record stigma in the labor market. Based on evidence from a survey experiment in which people with managerial experience were randomly assigned to job applicants with different narratives, we show that evaluations differ across reentry narratives. Drawing on prior theorizations and qualitative interviews with employers, we identify and describe three processes through which narratives impact evaluation and destigmatization: moral justification, social affinity signaling, and information salience.
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- Award ID(s):
- 2243822
- PAR ID:
- 10646794
- Publisher / Repository:
- Palgrave
- Date Published:
- Journal Name:
- American Journal of Cultural Sociology
- ISSN:
- 2049-7113
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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