Fast payoff: If you are analysing qualitative data from the Palghar IYCF protocol published 15 July 2026, this post shows a reproducible AI-enabled workflow to convert 460 mother–infant interviews, frontline-worker IDIs and process notes into thematic, segment and visual insights. The protocol is described in PLoS ONE and covers a 24-month, quasi-experimental mixed-methods study across 44 Anganwadi centres in Palghar, Maharashtra. In the first 150 words you get exact steps to ingest transcripts, harmonize codebooks, run thematic and cross-segment analysis, and produce stakeholder-ready visualizations using Evidano, a secure research stack that supports transcription, translation, thematic coding, co-occurrence networks and AI chat over your corpus. Note: this guidance is research-focused and non-diagnostic; follow approved IRB and consent/PII protections in the protocol.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that accelerates thematic and cross-segment analysis of IYCF data.
This post outlines a 7-step AI-enabled workflow to turn 460 mother–infant dyad transcripts, frontline-worker IDIs and weekly process data from the Palghar study (Oct 2024–Sep 2025; 24-month total study) into stakeholder-ready visuals and evidence briefs.
- Centralize audio, transcripts, diet cards and process sheets, then apply automated redaction, diarization, translation alignment and AI-assisted coding to save weeks of manual work.
- The 7-step workflow (ingest, clean/redact, translate, codebook, AI-assisted coding, cross-segment analysis, visualize/report) produces code frequencies, exemplar quotes and cross-segment comparisons across 44 AWCs.
- Security is preserved via end-to-end encryption and models not used to train third-party LLMs, enabling work with vulnerable groups while meeting compliance needs.
Findings Snapshot
| Item | Value | Source / Note |
|---|---|---|
| Publication date | 15 July 2026 | PLoS ONE |
| Study design | Quasi‑experimental, mixed methods (baseline, intervention, post) | Protocol |
| Sample (quantitative) | 460 mother–infant (6–12 mo) dyads (pre & post) | 44 Anganwadi Centres |
| Qualitative inputs | In-depth interviews (IDIs) with AWWs, ASHAs, supervisors; process notes | Situational analysis Feb–Sep 2024 |
| Intervention period | Oct 2024 – Sep 2025 (12 months intervention) | 24‑month total study |
| Registration | CTRI/2024/06/068427 (06 Jun 2024) | Clinical trial registry |
What the protocol collects (and why it matters)
The protocol collects structured WHO IYCF indicators, anthropometry, and rich qualitative inputs such as IDIs, observational notes, diet cards and weekly process indicators.
The study pairs eight WHO core IYCF indicators (initiation, exclusive breastfeeding, MDD, MMF, MAD, iron intake), anthropometry and monitoring data with qualitative transcripts; qualitative data is planned for verbatim transcription, NVivo analysis and synthesis with quantitative pre/post comparisons in SPSS.
- Units of analysis: mothers (n≈460 per round), AWWs (77 AWWs across 77 AWCs), supervisory staff and community stakeholders.
- Planned qualitative outputs: codebooks, themes on barriers/enablers, contextual recipes and SBC (social behavioural change) signal events.
- Monitoring data: weekly process indicators (feeds taken at AWCs, MDD/MMF/MAD counts, supplement uptake).
Qualitative analysis of IYCF practices: A 7-step AI-enabled workflow
The 7-step AI-enabled workflow below describes how to move from raw audio and text to evidence briefs and stakeholder visuals in weeks rather than months.
- 1) Ingest & centralize: Upload audio files, transcripts, diet cards and survey sheets. Evidano accepts mixed formats and spreadsheets, and supports custom dictionaries for local food terms.
- 2) Clean & redact: Run automatic speaker diarization, PII redaction, and normalization (for example, 'jowar' vs 'sorghum') so coding is consistent across sources.
- 3) Translate & align: Translate local languages with a custom glossary (preserve food names and cultural phrases) and align translated transcripts to original timestamps.
- 4) Build or import codebook: Import the study’s NVivo codebook (WHO/IYCF indicators) and extend with frontline-worker codes discovered in IDIs, then lock parent→subcode hierarchies.
- 5) AI-assisted coding: Auto-suggest codes at scale, then batch-validate with human reviewers and capture code frequencies and exemplar quotes with intercoder checks.
- 6) Cross-segment analysis: Compare themes by segment (AWC, village, AWW experience level, pre/post), and use co-occurrence networks to spot clusterable barriers (for example, food access and cultural restrictions).
- 7) Visualize & report: Generate word clouds, co-occurrence maps, hierarchical code trees and export readable evidence briefs for district officers, including process-indicator trend overlays.
Do more, faster with Evidano (mapped to the Palghar use case)
Problem: multilingual, messy transcripts → Solution
Evidano preserves semantic fidelity by offering transcription with custom dictionaries and translation matching local terminology used in Palghar, such as tribal food names and local units.
This approach ensures local phrases are retained for downstream coding rather than being lost in generic machine translation.
Problem: inconsistent coding across rounds → Solution
Evidano prevents inconsistency by allowing import of the study’s NVivo codebook or creation of hierarchical codes, then applying AI-assisted batch coding and producing intercoder reliability reports for audits.
This creates reproducible parent and subcode structures that are auditable across baseline and post rounds.
Problem: linking process indicators to narratives → Solution
Evidano links weekly process spreadsheets to thematic outputs to produce cross-segment frequency analyses and attach exemplar quotes to each metric for decision memos.
This makes it possible to present both trend data and qualitative explanatory quotes together in stakeholder-ready reports.
Problem: stakeholder-ready visuals & handoffs → Solution
Evidano expedites handoffs with one-click exports: co-occurrence networks, hierarchical code trees, slide-ready quote collections and a sharable AI chat over your corpus so district officers can query the evidence without re-running analysis.
These export formats are designed for policy memos and presentations to district and state officers.
Security & compliance
Evidano uses end-to-end encryption and proprietary LLMs tuned for qualitative research, and data is never used to train third-party models.
These security practices are crucial when working with vulnerable groups and personally identifiable information.
Checklist: 10 practical QA steps before analysis
The checklist lists 10 QA steps to confirm before running batch AI coding.
- All transcripts timestamped and linked to audio.
- PII redaction completed and consent documented.
- Custom glossary uploaded for local food and terms.
- Baseline codebook imported and agreed by two or more analysts.
- Sample intercoder reliability test (kappa) passed.
- Process indicator spreadsheet keys match qualitative IDs (AWC codes).
- Pre/post flags present on records for segment comparison.
- Diet cards digitized and normalized.
- Weekly monitoring logs ingested for time-series overlays.
- Export templates defined for stakeholders (one-pager and slide deck).
FAQ: Common questions from researchers
Q: How do I compare themes across the 44 AWCs?
Use cross-segment analysis to compute theme prevalence per AWC and visualize differences with heatmaps and co-occurrence networks, filtering by time to show pre/post change.
This lets you identify AWCs with distinct barrier clusters and track shifts over the intervention period.
Q: Can AI preserve cultural phrasing important to interpretation?
Yes, upload a custom dictionary and set translation conservatism so local phrases are retained as labels rather than auto-translated away.
Preserving cultural phrasing improves interpretability of themes and maintains fidelity of quoted material used in briefs.
Wrapping up: next steps and CTA
The next steps are to centralize audio, transcripts and process sheets, then run the 7-step workflow above to cut coding time, improve reproducibility and produce stakeholder-ready visuals.
If you are preparing to analyse the Palghar IYCF protocol data, see the protocol in PLoS ONE.
- Ready for a trial? See how Evidano ingests mixed inputs, preserves local terms, runs thematic and cross-segment analyses and exports policy-ready briefs at Evidano, or Try Evidano for free.
Ethics note: This guidance is research-focused and non-diagnostic; always follow approved IRB procedures and the consent and PII protections described in the protocol.