Asking for Care Online: A Content Analysis of Insurance-Referenced Posts on r/Askdocs

Background and Purpose: Individuals increasingly seek advice, reassurance, and support in online health forums as they navigate the healthcare system. Such forums function as anonymous online waiting rooms, where individuals seek guidance about symptom severity, access to health services, and appropriate levels of care. The subreddit r/AskDocs is a high-volume public platform where Reddit users can describe symptoms and decision making in real time. As part of a broader project examining help-seeking and care navigation narratives in online reddit based health communities, this analysis focuses specifically on posts that reference health insurance status or insurance related constraints. While prior work has evaluated large language models (LLMs) for structuring r/AskDocs data, posts that specifically reference insurance have not been analyzed as a focused subset to determine dominant help-seeking concerns. The purpose of this study is to classify insurance-referenced r/AskDocs posts using an inductively developed, single-label topic scheme.

Methods: A mixed human-AI content analysis was conducted using r/AskDocs posts from 2013-2022. From a corpus of 1,048,576 posts, those referencing “insurance” in the title and/or initial post were identified, yielding a sample of N = 15,140. Using GPT-5, self-reported demographic information such as age, gender, race/ethnicity, and geographic location were extracted when provided, and insurance status was classified as insured, uninsured or unclear. An inductive, single-label topic scheme and definitions were then developed. A 300-post subset was used to label and establish a human-coded benchmark; an overlapping portion was independently double coded by additional coders to assess reliability and resolve discrepancies. Finally, a zero-shot prompt designed to produce consistent single-label topic outputs was applied. Topic assignment performance was evaluated by comparing GPT-5 classifications to the human-coded benchmark prior to scaling across all insurance-referenced posts.

Results: Insurance status was classified as uninsured (n = 5,478), insured (n = 5,129), or unclear (n = 1,173). Among adults aged 18 and older, the mean age was 28.16 years. Race and gender were associated with insurance status. In adjusted models, Black/African American posters had higher odds of being uninsured than Caucasian posters (OR = 1.74, p = .003), Hispanic/Latino posters had more than twice the odds of being uninsured (OR = 2.42, p < .001), and male posters had higher odds of being uninsured than female posters (OR = 1.34, p < .001). Older age was associated with lower odds of being uninsured (OR = 0.98). Topic distribution also differed significantly by insurance status, χ²(106) = 1002.26, p < .001. Uninsured posters were more likely to be represented in access-related concerns, whereas insured posters were more likely to be represented in posts involving unclear diagnoses and feeling dismissed by providers.

Conclusions and Implications: Insurance-related help-seeking on r/AskDocs reflects inequitable participation in healthcare access and navigation. This study highlights that insurance disparities are visible in online medical help-seeking and suggests that online health forums may offer a useful site for understanding how coverage status shapes care-seeking experiences. Insights from this study may outreach, enrollment support, care navigation, and policy efforts aimed at improving equitable healthcare access.