Five Hajj Sun-Safety Themes: AI Qualitative Analysis

Researchers should code heat-related illness and UV-related harm as related but distinct concepts, according to the 2026 PLoS One study.

The study reports that participants described exhaustion, dizziness, dehydration, and fainting, but rarely raised UV-specific effects such as sunburn or eye irritation. A coding framework that combines every sun-related statement could hide important differences in prevention needs.

Qualitative teams should code cultural and religious meanings as contextual influences that can either discourage or support protective behavior, according to the Hajj interviews.

The study reported that some participants viewed enduring sun exposure as part of spiritual hardship, while other participants described umbrella use as increasingly accepted. The finding suggests that intervention research should test culturally compatible messages instead of treating culture as a fixed obstacle.

Multilingual qualitative research requires translation checks because language differences can alter the meaning, emphasis, or cultural context of participant accounts.

The study reported that nurses faced difficulty communicating with pilgrims who did not speak Arabic or English. One nurse called language "the biggest challenge" and said that sign-language translators were sometimes needed. The researchers addressed translation risk through bilingual translation, a 20% audit, and participant transcript review.

Researchers should preserve participant quotations, source context, language information, subgroup identity, and an auditable link between codes and original transcript passages when using AI.

The Hajj study used multiple coders, consensus discussions, member checking, reflexive practices, and trustworthiness criteria. AI can accelerate organization and comparison, but those safeguards remain important for interpretation and reporting.

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.

Evidano can organize interview transcripts into thematic, content, frequency, and cross-segment analyses, helping researchers examine themes such as heat risk, UV knowledge, cultural influence, and access barriers across pilgrims and nurses. Researchers can review the underlying material through Evidano’s qualitative research features.

Evidano supports translation workflows with custom dictionaries, which can help research teams standardize domain-specific terms while reviewing multilingual qualitative data.

For studies involving Arabic, English, or other languages, researchers should still validate translated meanings against the original transcript and retain human review. Evidano’s translation capabilities are relevant for organizing multilingual research, not for replacing methodological quality checks.

Evidano supports cross-segment analysis, allowing teams to compare how different participant groups describe the same issue.

A health-research team could compare pilgrim and nurse perspectives on UV awareness, communication barriers, product cost, and cultural clothing constraints, then use frequency and thematic outputs to identify where perspectives converge or diverge.

Evidano provides AI chat over uploaded documents and analyses, allowing researchers to ask focused questions across an evidence set.

A team could ask which barriers were reported by nurses but not pilgrims, which quotations support the education recommendation, or how often participants distinguished heat from UV exposure. Researchers should verify AI-generated answers against source passages before publication.

Evidano encrypts data and does not use customer data to train third-party models, according to Evidano’s platform information.

Research teams handling interview transcripts should also follow their institutional ethics approvals, consent requirements, de-identification procedures, and data-retention policies.