Evidence map›Paper›PMID 42081804›Full record

ArticleJMIR formative research2026

Peer Support in Online Women's Health Communities: Mixed Methods Formative Analysis of Reddit Discourse.

Kimia Tuz Zaman, Wordh Ul Hasan, Nova Ahmed, Juan Li

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kimia Tuz Zaman *Department of Computer Science, North Dakota State University, Quentin Burdick Building Room 258, 1320 Albrecht Boulevard, Fargo, ND, 58105, United States, 1 701-231-9662.ORCID 0000-0002-3812-7547
Wordh Ul Hasan *Department of Computer Science, Tuskegee University, Tuskegee, AL, United States.ORCID 0009-0000-0542-699X
Nova Ahmed *Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.ORCID 0000-0002-7715-1742
Juan Li *Department of Computer Science, North Dakota State University, Quentin Burdick Building Room 258, 1320 Albrecht Boulevard, Fargo, ND, 58105, United States, 1 701-231-9662.ORCID 0000-0002-7668-5996

Funding

National Science Foundation 2218046
6 · The paper itself

Abstract

Background: Stigmatized women's health issues, such as polycystic ovary syndrome (PCOS) and endometriosis, are often marginalized or dismissed in traditional clinical settings. This drives individuals to seek peer support in anonymous online communities such as Reddit. While these digital platforms host critical discussions, they are often designed as static information repositories, failing to account for the complex emotional, temporal, and cultural dynamics that shape users' support needs. There is a disconnect between the lived experiences of users-particularly feelings of clinical dismissal and the need for culturally specific advice-and the design of the sociotechnical systems they rely on. Objective: This study aimed to deconstruct support practices in online women's health forums to provide a formative basis for designing more responsive digital health systems. We analyzed the intersections of discussion topics, emotional expression, temporal shifts (specifically the impact of the COVID-19 pandemic), and culturally situated discourse to identify unmet user needs and effective peer-support patterns. Methods: We conducted a large-scale, mixed-methods analysis of 4995 posts and 460,317 comments from 5 major women's health subreddits (r/WomensHealth, r/TwoXChromosomes, r/BirthControl, r/Endometriosis, and r/PCOS). Computational methods included Latent Dirichlet Allocation for topic modeling, Valence Aware Dictionary for Sentiment Reasoning for sentiment analysis, and the NRC Emotion Lexicon for granular emotion classification. We segmented the data into pre-, during-, and post-COVID-19 periods to analyze temporal shifts. This quantitative analysis was complemented by a 2-phase qualitative thematic analysis to identify and characterize engagement patterns within 147 validated culturally situated threads. Results: Our analysis revealed that the most prevalent and emotionally negative topic was "Pain & Doctor Visits," which was uniquely characterized by high levels of fear and sadness linked to systemic clinical dismissal. The COVID-19 pandemic triggered a significant topical "turn inward," with discussions shifting away from social or political issues and toward somatic concerns (eg, "PCOS" "Pain & Doctor Visits"). Paradoxically, this period saw a simultaneous rise in both negative emotions (eg, fear and sadness) and expressions of community trust. Critically, our qualitative analysis of culturally situated discourse uncovered a consistent three-stage "playbook" for effective support: (1) Affirmation to establish psychological safety and validate cultural experiences; (2) Information Scaffolding to provide actionable, culturally tailored advice; and (3) Intercultural Bridging to facilitate community-wide learning and empathy. Conclusions: Online health forums operate as essential, resilient sociotechnical infrastructures that actively compensate for failures and gaps in formal health care. The "Affirmation-Scaffolding-Bridging" model identified in our research provides a clear, formative framework for designing future digital health interventions. These findings can guide the development of new platforms that are emotionally aware, culturally responsive, and adaptive to user needs and external crises.

Indexed as

Peer GroupSocial MediaSocial SupportWomen's HealthCOVID-19Digital MediaFemaleHumansInternetPandemicsSARS-CoV-2Self-Help Groupscultural competencyformative researchonline health communitiespeer supportsocial supportsystemic clinical dismissalwomen’s health

Identifiers

PMID42081804
PMCPMC13138790

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.