Beyond Judgment: Exploring Large Language Models as Non-Judgmental Support for Maternal Mental Health

Fuente: arXiv
Guardado en:
Detalles Bibliográficos
Autores principales: Sharmin, Shayla, Ratna, Sadia Afrin
Formato: Preprint
Publicado: 2026
Materias:
Acceso en línea:
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866915841531969536
author Sharmin, Shayla
Ratna, Sadia Afrin
author_facet Sharmin, Shayla
Ratna, Sadia Afrin
contents In the age of Large Language Models (LLMs), much work has already been done on how LLMs support medication advice and serve as information providers; however, how mothers use these tools for emotional and informational support to avoid social judgment remains underexplored. This study conducted a 10-day mixed-methods exploratory survey ($N=107$) to investigate how mothers use LLMs as a non-judgmental resource for emotional support and regulation, and for situational reassurance. Our findings show that mothers are asking LLMs various questions about childcare to reassure themselves and avoid judgment, particularly around childcare decisions, maternal guilt, and late-night caregiving. Open-ended responses also show that mothers are comfortable with LLMs because they do not have to think about social consequences or judgment. Although mothers use LLMs for quick information or reassurance to avoid judgment, over half of the participants value human warmth more than LLMs; however, a significant minority, especially those in joint families, consider LLMs to avoid human judgment. These findings help understand how LLMs can be framed as low-risk interaction support rather than a replacement for human support, and highlight the role of social context in shaping emotional technology use.
format Preprint
id arxiv_https___arxiv_org_abs_2602_06678
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Beyond Judgment: Exploring Large Language Models as Non-Judgmental Support for Maternal Mental Health
Sharmin, Shayla
Ratna, Sadia Afrin
Human-Computer Interaction
In the age of Large Language Models (LLMs), much work has already been done on how LLMs support medication advice and serve as information providers; however, how mothers use these tools for emotional and informational support to avoid social judgment remains underexplored. This study conducted a 10-day mixed-methods exploratory survey ($N=107$) to investigate how mothers use LLMs as a non-judgmental resource for emotional support and regulation, and for situational reassurance. Our findings show that mothers are asking LLMs various questions about childcare to reassure themselves and avoid judgment, particularly around childcare decisions, maternal guilt, and late-night caregiving. Open-ended responses also show that mothers are comfortable with LLMs because they do not have to think about social consequences or judgment. Although mothers use LLMs for quick information or reassurance to avoid judgment, over half of the participants value human warmth more than LLMs; however, a significant minority, especially those in joint families, consider LLMs to avoid human judgment. These findings help understand how LLMs can be framed as low-risk interaction support rather than a replacement for human support, and highlight the role of social context in shaping emotional technology use.
title Beyond Judgment: Exploring Large Language Models as Non-Judgmental Support for Maternal Mental Health
topic Human-Computer Interaction
url https://arxiv.org/abs/2602.06678