Using large language models to create lexicons for interpretable text models with high content validity: The suicide risk lexicon.

Daniel M. Low, Osiris Rankin, Daniel D. L. Coppersmith, Kate H. Bentley, Matthew K. Nock, Satrajit S. Ghosh

Open source

DOI: 10.1037/abn0001152
Published: 2026-09-24
Container: Journal of Psychopathology and Clinical Science
Publisher: American Psychological Association (APA)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1037/abn0001152,
  title = {Using large language models to create lexicons for interpretable
						text models with high content validity: The suicide risk
						lexicon.},
  author = {Daniel M. Low and Osiris Rankin and Daniel D. L. Coppersmith and Kate H. Bentley and Matthew K. Nock and Satrajit S. Ghosh},
  year = {2026},
  journal = {Journal of Psychopathology and Clinical Science},
  doi = {10.1037/abn0001152},
  url = {https://doi.org/10.1037/abn0001152}
}

RIS

TY  - JOUR
TI  - Using large language models to create lexicons for interpretable
						text models with high content validity: The suicide risk
						lexicon.
AU  - Daniel M. Low
AU  - Osiris Rankin
AU  - Daniel D. L. Coppersmith
AU  - Kate H. Bentley
AU  - Matthew K. Nock
AU  - Satrajit S. Ghosh
PY  - 2026
JO  - Journal of Psychopathology and Clinical Science
DO  - 10.1037/abn0001152
UR  - https://doi.org/10.1037/abn0001152
ER  -

APA

Low, D. M., Rankin, O., Coppersmith, D. D. L., Bentley, K. H., Nock, M. K., & Ghosh, S. S. (2026). Using large language models to create lexicons for interpretable text models with high content validity: The suicide risk lexicon.. Journal of Psychopathology and Clinical Science. https://doi.org/10.1037/abn0001152

Source records

crossref · retrieved 2026-11-10T08:46:21.495Z