Between Myths and Metaphors: Rethinking LLMs for SRH in Conservative Contexts

TL;DR

This paper examines how large language models can improve sexual and reproductive health communication in conservative, low-resource settings like Pakistan, highlighting challenges and proposing design strategies.

Contribution

It introduces a framework for understanding indirect SRH communication and evaluates LLM performance in this context, offering practical design recommendations.

Findings

LLMs struggle with semantic drift and myths in SRH communication.

Two axes of communication influence how information is conveyed.

Empirical analysis reveals key challenges in LLM interpretation of SRH data.

Abstract

Low-resource countries represent over 90% of maternal deaths, with Pakistan among the top four countries contributing nearly half in 2023. Since these deaths are mostly preventable, large language models (LLMs) can help address this crisis by automating health communication and risk assessment. However, sexual and reproductive health (SRH) communication in conservative contexts often relies on indirect language that obscures meaning, complicating LLM-based interventions. We conduct a two-stage study in Pakistan: (1) analyzing data from clinical observations, interviews, and focus groups with clinicians and patients, and (2) evaluating the interpretive capabilities of five popular LLMs on this data. Our analysis identifies two axes of communication (referential domain and expression approach) and shows LLMs struggle with semantic drift, myths, and polysemy in clinical interactions. We contribute: (1) empirical themes in SRH communication, (2) a categorization framework for indirect communication, (3) evaluation of LLM performance, and (4) design recommendations for culturally-situated SRH communication.