AI-assisted qualitative analysis helps mixed-methods teams synthesize interviews, surveys, and documents faster while preserving traceability. Researchers running explanatory sequential designs need reproducible pipelines to move from numeric trends to participant narratives. The primary keyword ai-assisted qualitative analysis describes workflows that combine automated transcription, coded thematic extraction, and joint displays. According to the PLOS One protocol by Phonyiam et al. (2026), the exemplar study translates a postnatal well-being instrument into Thai and pairs a 50-person survey with 12 in-depth interviews, offering a clear test case for AI-enabled synthesis.
Key Takeaways
According to the PLOS One protocol by Phonyiam et al. (2026) (PLOS One), an explanatory sequential mixed-methods study will translate a postnatal well-being scale into Thai, survey 50 postnatal women with type 2 diabetes, and conduct 12 follow-up interviews to triangulate social determinants of health (SDoH) with lived experience.
- 50 survey participants will be recruited, and 12 semi-structured interviews will be nested from that sample, according to Phonyiam et al. (2026).
- The protocol was published on July 29, 2026, and recruitment began in September 2025 with data collection expected to finish by December 2026, per Phonyiam et al. (2026).
- The Thai translation validation reported that “the I-CVI and S-CVI were both 1.0, ” indicating unanimous expert agreement in July 2026, per Phonyiam et al. (2026).
What happened and how the study works
The PLOS One protocol by Phonyiam et al. (2026) answers how to design an explanatory sequential mixed-methods study that links quantitative scores to qualitative narratives.
According to Phonyiam et al. (2026), Phase 1 produced a culturally adapted Thai version of the Postnatal Well-being in Transition scale using a six-stage Beaton translation process and expert validation.
According to Phonyiam et al. (2026), Phase 2 will recruit 50 postnatal women (delivered within the past six weeks) for a survey covering demographics, five SDoH domains, and a 30-item well-being questionnaire, and then select 12 participants for semi-structured phone interviews based on extreme scores.
According to Phonyiam et al. (2026), the study will integrate findings with side-by-side joint displays and a three-step interpretive framework to generate meta-inferences across quantitative and qualitative strands.
Phonyiam et al. (2026) state explicitly that “we expect to complete data collection by December 2026, and the results are projected to be finalized by June 2027, ” and they also report that “the I-CVI and S-CVI were both 1.0, ” both quotes from the protocol (Phonyiam et al., 2026).
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| July 29, 2026 | Protocol published | PLOS One article | Public protocol enables reproducibility and method sharing |
| September 2025 (started) | Recruitment start | September 2025 | Fieldwork timeline began early to capture postnatal window |
| 2026 (planned) | Survey sample size | 50 participants | Descriptive quantitative phase for pattern detection |
| 2026 (planned) | Qualitative interviews | 12 participants | Purposeful nested sampling to explain quantitative extremes |
| July 2026 (validation) | Content validity indices | I-CVI = 1.0, S-CVI = 1.0 | Expert panel consensus on Thai instrument content |
Implications for qualitative researchers and mixed-methods teams
Researchers should treat nested sampling as a design lever to connect numeric patterns to rich narratives, as recommended in the PLOS One protocol by Phonyiam et al. (2026).
- Design: Phonyiam et al. (2026) show that embedding 12 interviews inside a 50-person survey provides manageable depth for triangulation without overextending resources.
- Measurement: Phonyiam et al. (2026) demonstrate that forward/back translation and expert review can yield unanimous content validity (I-CVI and S-CVI = 1.0), but they caution that formal psychometric validation remains necessary for cultural equivalence.
- Integration: Phonyiam et al. (2026) recommend statistics-by-themes joint displays and a three-step interpretive framework to classify convergence, expansion, and discordance for trustworthy meta-inferences.
How Evidano Helps: mapping protocol needs to AI-enabled features
Problem: small mixed-methods teams need fast transcription and traceable coding
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano automates accurate transcription with custom dictionaries and PII redaction to speed the transcription Phonyiam et al. (2026) describe, and it stores audio-to-text links for auditability.
Evidano feature page: Evidano features.
Problem: linking quantitative scores to qualitative excerpts for joint displays
According to Phonyiam et al. (2026), joint displays require pairing thematic codes with numeric means and SDs; Evidano generates thematic analyses and exports coded excerpts alongside per-case metadata for side-by-side comparison.
Use Evidano outputs to populate the statistics-by-themes matrices the PLOS One protocol recommends, reducing manual crosswalk work.
Problem: multilingual instrument translation and consistent terminology
Phonyiam et al. (2026) used a six-stage translation process; Evidano supports uploaded translated instruments and multilingual coding, and offers a speech-to-text pipeline with custom dictionaries to preserve domain terms across languages.
Evidano preserves provenance so teams can trace which transcript segment produced each code, aiding the procedural resolution of discordance the protocol prescribes.
FAQ: ai-assisted qualitative analysis
How can AI help produce the statistics-by-themes joint displays that Phonyiam et al. (2026) recommend?
Answer: AI can auto-code transcripts, extract exemplar quotes, and attach participant IDs and quantitative scores so you can build joint displays rapidly.
Supporting detail: According to Phonyiam et al. (2026), joint displays pair qualitative themes with means and standard deviations; Evidano exports code-to-case matrices and excerpt tables ready for side-by-side comparison.
Is automated transcription reliable enough for Thai interviews in a mixed-methods study?
Answer: Automated transcription with a domain-tuned custom dictionary is a reliable starting point, but protocol-level review is still recommended.
Supporting detail: Phonyiam et al. (2026) required verbatim Thai transcripts and expert validation; Evidano’s transcription plus human review supports the same workflow while cutting initial labor.
Will AI change the procedural resolution of discordance between quantitative and qualitative findings?
Answer: AI does not replace human judgement, but it speeds the identification of discordant cases and patterns for human-led reconciliation.
Supporting detail: Phonyiam et al. (2026) outline a three-step reconciliation process; AI-assisted filtering and case retrieval make steps (1) and (2) faster so teams can focus on interpretive synthesis.
Can AI maintain confidentiality and audit trails required by ethical protocols?
Answer: Yes, when platforms use encryption, access controls, and PII redaction, they can meet standard research ethics needs.
Supporting detail: Phonyiam et al. (2026) emphasize secure storage and de-identification for transcripts; Evidano provides encryption and redaction features and documents provenance for audits.
Conclusion & Next Steps
The PLOS One protocol by Phonyiam et al. (2026) provides a clear, reproducible exemplar for explanatory sequential mixed-methods work on SDoH and postnatal wellbeing in type 2 diabetes.
AI-assisted qualitative analysis can accelerate transcription, coding, and joint-display construction while preserving the human interpretation steps the protocol requires, according to Phonyiam et al. (2026).
If you are running a mixed-methods study or planning a nested interview sample, consider tools that combine accurate speech-to-text, secure data handling, and exportable code-to-case matrices.
To experiment with an AI platform built for qualitative research, Try Evidano for free.