Qualitative analysis of mental health recovery: Thailand

For UX and qualitative teams, treat language variants as analytic assets by tagging dialectal phrases and tracking frequency across segments (service users vs carers vs HCP).

Run co-occurrence analyses to surface how concepts (hope, family, alcohol) cluster with functional versus personal recovery language.

For policy and health analysts, use coded touchpoints to map service bottlenecks (for example, limited subdistrict capacity, medication side-effects) and quantify how often they appear across interviews.

Translate thematic findings into measurable indicators for training or funding priorities (for example, percent of interviews referencing family support as a recovery enabler).

For clinicians and program designers, extract direct quotes grouped by theme for training materials and co-design workshops while ensuring quotes retain cultural idioms via glossaries.

Prioritise interventions identified as feasible at primary care level, such as peer support, village health volunteers, and community alcohol controls.

Evidano accelerates bilingual transcription and glossary preservation with configurable dictionaries and human-review workflows that mirror the study’s methods.

Problem: the study relied on bilingual transcription and a study glossary to preserve dialect and cultural meaning.

Evidano: automated transcription with custom dictionaries plus human-review workflow, and translation that imports glossaries so terms like merit and karma keep contextual notes.

Evidano reduces manual NVivo workload by producing reproducible, hierarchical themes and rapid cross-segment reports that match the study’s comparison of service users, carers, and HCP.

Problem: manual NVivo coding is time-consuming and hard to scale when comparing service users, carers, and HCP.

Evidano: import transcripts, run AI-assisted inductive coding, produce hierarchical themes to subcodes, and run cross-segment frequency and co-occurrence reports in minutes.

Evidano produces shareable artifacts for member-checking and policy briefs, such as touchpoint lists, clickable quote bundles, and co-occurrence networks.

Problem: feedback workshops need clear artifacts (touchpoints, quotes, word clouds).

Evidano: export word clouds, co-occurrence networks, and clickable quote sets to use directly in member-checking or experience-based co-design sessions.

Evidano supports secure workflows required for mental health research, including encryption, PII redaction, and a policy that customer data are not used to train third-party models.

Problem: mental health data requires strict privacy and cannot be used to train public models.

Evidano: end-to-end encryption, PII redaction, and guarantee that your data are never used to train third-party models.

Compare segments reliably by using consistent code definitions, automated frequency counts, and normalization by document counts for each segment.

Use consistent code definitions, run automated frequency counts, and normalise by the number of documents in each segment; Evidano produces cross-segment tables and significance flags.

Preserve cultural nuance by building a project glossary, marking untranslatable idioms as tokens, and reviewing automated translations with a bilingual reviewer.

Build a project glossary, mark untranslatable idioms as tokens, and review automated translations with a bilingual reviewer; Evidano supports glossary import and side-by-side source-target inspection.

AI can be safe for sensitive transcripts when you anonymise and redact PII, restrict access, and use platforms that guarantee no third-party model training.

Best practice: anonymise and redact PII, restrict access, and use platforms that guarantee no third-party model training; Evidano encrypts data and does not share your data to train external models.