Cut to Insight: Qualitative Analysis of IBD Stigma

The PLOS One study (June 26, 2026) found 63.0% of 146 IBD patients reported at least one stigma experience, concentrated in social avoidance (45.9%) and public discomfort (36.3%). Researchers should pair thematic coding of binary items and free-text with cross-segment comparison (age, disease subtype, RSES) to turn prevalence into targeted interventions. Use AI-assisted workflows to accelerate coding, generate heatmaps and segment comparisons, and produce stakeholder-ready visuals while preserving privacy.

• 63.0% prevalence across n=146, study period Dec 1, 2025 – Jan 10, 2026, stigma defined as ≥1 'Yes' on 10 IBD-specific items (KR-20 = 0.83).
• Primary manifestations were social avoidance (45.9%) and public discomfort (36.3%), suggesting behavioral avoidance is more common than overt discrimination.
• Recommended analytic focus: thematic coding of short disclosures, cross-segment tests by age and RSES, and visual outputs (heatmaps, co-occurrence networks) for stakeholders.

Fast take: The PLOS One study (June 26, 2026) reported that 63.0% of 146 Turkish IBD patients endorsed at least one stigma-related item, with younger age and lower Rosenberg Self‑Esteem Scale scores independently associated with perceived stigma. Read the original paper at PLOS One.

• Study dates: Dec 1, 2025 – Jan 10, 2026; design: cross-sectional online survey; stigma defined as ≥1 'Yes' on 10 IBD-specific items (KR-20 = 0.83).
• Why analysts care: the paper produces categorical responses and short disclosures ideal for thematic coding, cross-segment comparison, and visualization.

Study design: The authors collected data using an online Google Forms survey with four blocks (demographics, disease features, RSES, and 10 binary stigma items). Snowball sampling from a tertiary clinic produced n=146; responses were analyzed with nonparametric tests and a logistic regression (age and RSES entered as continuous variables).

• Stigma items: concrete yes/no questions (e.g., 'Do you avoid social activities because of your IBD? ').
• Visual: authors used a heatmap of affirmative responses (Python seaborn/matplotlib).
• Limitations to note: no standardized IBD stigma scale, cross-sectional design, potential sampling bias (online/snowball).

Two-week workflow: follow these six steps to go from dataset import to a stakeholder-ready memo. Step 1: Import survey CSV or transcripts into Evidano; map variables (age, diagnosis age, subtype, RSES). Step 2: Run automated cleaning and a first-pass topic model to surface candidate stigma themes. Step 3: Use AI-assisted coding to apply or refine a codebook; validate with 10–15 manual checks. Step 4: Produce cross-segment frequency tables and a heatmap mirroring the paper (stigma items × subgroup). Step 5: Generate an executive memo with top 5 themes, representative quotes (PII redacted), and recommended interventions. Step 6: Share interactive visualizations with clinical and UX stakeholders; schedule targeted qualitative follow-ups using AI avatar interviewers if needed.

Conclusion: The PLOS One study (June 26, 2026) shows stigma in IBD is common and concentrated in social discomfort and avoidance, actionable signals for researchers and care teams. Reproduce and expand that work by combining thematic coding with cross-segment analysis to design targeted psychosocial supports. Ready to try this on your dataset? Try Evidano for free. Ethics note: this guidance is research-focused and non-diagnostic; follow local IRB and consent rules for patient data.