Qualitative analysis of autism caregiving

Key Takeaways: the PLOS ONE study (published 17 July 2026) used semi-structured interviews and reflexive thematic analysis to identify six themes that highlight system gaps, cultural variation, and neuroaffirmation in autism caregiving in England. Use a reproducible, two-week workflow (upload → transcribe → code → compare → report) to scale these insights with Evidano.

• The PLOS ONE study (Farooq et al.) published 17 July 2026, used n=12 semi-structured interviews and a survey pool of n=150 to identify six high-level themes.
• Researchers can reproduce the study’s approach with a pragmatic seven-step runbook that fits a two-week single-researcher timeline using Evidano’s ingestion, AI-assisted coding, and exportable visuals.
• Designers and policy teams should map repeated service contact points (diagnostic waits, SALT/OT referrals) and build culturally responsive, neuroaffirming supports based on qualitative themes.

Fast take: a PLOS ONE article by Farooq et al. (published 17 July 2026) used semi-structured interviews and reflexive thematic analysis to study parents' caregiving experiences in England.

• Why this matters: qualitative analysis of autism caregiving reveals where services, policy, and product teams should intervene, including diagnostics, SALT/OT access, culturally responsive support, and benefit navigation.
• What you'll get: a compact workflow to reproduce these insights at scale with Evidano (transcription → thematic coding → cross-segment comparison → visual reporting).
• Read the full article at PLOS ONE.

How the study worked: Farooq et al. ran semi-structured interviews (60–90 mins), transcribed in NVivo, then performed reflexive thematic analysis from a constructionist stance with institutional ethical approval.

• Design and analysis: semi-structured interviews (60–90 mins), NVivo transcription, reflexive thematic analysis, ethical approval UoL2024_17776; received 15 March 2026, accepted 1 July 2026, published 17 July 2026.
• Data: interview transcripts (de-identified) plus survey metadata (n=150).
• Analytic approach: iterative coding and reflexive memos produced six high-level themes across semantic and latent levels; reflexive thematic analysis emphasises researcher interpretation over inter-coder reliability, so document reflexive memos and coding decisions.

Two-week workflow: a pragmatic seven-step runbook (two weeks, single researcher plus Evidano) reproduces the study’s insights and supports publication-ready outputs.

• 1) Upload audio and survey CSVs; set custom dictionary (names, local terms).
• 2) Auto-transcribe; run PII redaction; QA a 10% sample.
• 3) Import or draft initial codebook based on the paper’s six themes; apply AI-assisted auto-coding.
• 4) Review and refine codes; create hierarchical themes and subcodes.
• 5) Run cross-segment frequency and co-occurrence analyses (for example, gender × stigma; diagnosis wait × help-seeking).
• 6) Generate visuals (thematic map, quote bank) and ask Evidano AI to draft a one-page policy brief.
• 7) Export methods appendix (audit trail) for publication or funder review.

Ethics note: this post interprets published qualitative research for methodological use, and findings are research-focused and non-diagnostic.

When working with caregiver or clinical data, maintain consent, anonymisation, and institutional ethics approvals.

Wrapping up: if you are running a qualitative analysis of autism caregiving or any SEND-focused study and want to scale coding, compare cultural segments, and produce publishable visuals in days rather than months, Evidano can reproduce the pipeline above securely with audit trails.

Try a pilot: upload five interviews and a survey export to validate codes and run a cross-segment comparison in under a week, or Try Evidano for free.

Read the source paper at PLOS ONE for full methods and findings.