Fast payoffs for researchers and program teams: this PLOS One protocol (published 15 July 2026) describes a 24-month quasi-experimental mixed-methods intervention in Palghar, Maharashtra (n=460 mother–infant dyads, 44 Anganwadi Centres). Read the protocol: PLOS One. If your team needs repeatable thematic analysis of interviews, IDIs, and weekly process notes from that kind of field trial, this post shows a compact AI-enabled workflow to run qualitative analysis of IYCF practices and compare pre/post outcomes, using Evidano to automate coding, cross-segment comparisons, and stakeholder-ready visuals (learn more at Evidano). Ethics note: this is research-focused, non-diagnostic reporting; always follow local IRB consent and data protection practices.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that streamlines thematic coding, cross-segment comparisons, and stakeholder-ready outputs for studies like the Palghar IYCF trial. This post lays out a compact, reproducible two-week workflow to convert Palghar trial transcripts, process logs, and diet-card notes into actionable themes and visuals tied to IYCF indicators.
- The Palghar trial is a 24-month quasi-experimental mixed-methods protocol (Oct 2024 start) with n=460 mother–infant dyads and 44 Anganwadi Centres, PLOS One (published 15 Jul 2026).
- A reproducible two-week workflow lets teams ingest audio and text, auto-transcribe, generate themes, review a codebook, run cross-segment analysis, and produce a stakeholder brief and visuals.
- Evidano supports custom dictionaries for local terms, optional PII redaction, encrypted data storage, and proprietary models that do not use customer data to train third-party models.
- Tie qualitative themes directly to measurable IYCF indicators (MDD, MMF, MAD, timely CF) and use weekly process logs to detect early implementation drift.
Findings Snapshot
| Metric | Value / Detail | Source | Implication |
|---|---|---|---|
| Study design | Quasi-experimental mixed-methods, 3 phases (baseline, intervention, post) | PLOS One (published 15 Jul 2026) | Pre/post comparison plus thematic IDIs |
| Sample size | 460 mother–infant (6–12m) dyads at baseline and 460 at endline | Protocol (sample calculation) | Sufficient power to detect ~10% change in timely CF |
| Clusters | 44 Anganwadi Centres across 6 subcentres (77 AWCs in PHC area) | Study framework | Clustered implementation; useful for segment analysis |
| Intervention length | 12-month intervention (Oct 2024 start), 24 months total | Protocol timeline | Repeated process indicators and monthly monitoring |
| Qualitative inputs | In-depth interviews with AWWs, ASHAs, supervisors; IDIs Feb–Sep 2024 | Methods section | Rich text corpus for thematic analysis |
What happened (plain English)
The study tested an SBCC-led package delivered by frontline workers combining training, pictorial IEC, weekly food charts, live recipe demos, hot cooked complementary feeds at Anganwadi centres, and micronutrient supplements. Process monitoring used weekly indicators, and outcomes measured IYCF core indicators (MDD, MMF, MAD, timely CF).
- Baseline qualitative work: IDIs with district officials and frontline workers to map barriers and counselling gaps (Feb–Sep 2024).
- Quantitative baseline: structured survey and anthropometry across 460 infants (6–12 months).
- Intervention: Oct 2024–Sep 2025 with hot meals, SBCC, recipe videos, diet cards, WhatsApp support.
- Post-intervention: six-month endline and qualitative follow-up (complete by Jan–Feb 2026).
- Registration: CTRI/2024/06/068427 (registered 06 June 2024).
Why this protocol matters for qualitative researchers
This protocol matters because mixed-methods trials generate multiple text streams (IDI transcripts, diet-card notes, monitoring logs, WhatsApp message threads, supervisor reports) that create scale and complexity for manual coding. Reproducible thematic coding across timepoints and segments is essential for valid comparisons and program learning.
- You need reproducible thematic coding across timepoints (baseline vs endline) and segments (mothers vs AWWs vs ASHAs).
- Process indicators (weekly logs) are ideal for time-series content analysis to detect adoption patterns and implementation gaps.
- Cultural and language variation in tribal contexts requires careful handling of translation and local terms during coding.
How Evidano helps: operationalizing qualitative analysis of IYCF practices
Import & prepare heterogeneous text
Evidano ingests IDI transcripts, audio files, WhatsApp exports, and structured diet-card spreadsheets into one project workspace. Automatic transcription with a custom dictionary for local terms is available, plus optional PII redaction to match IRB requirements.
Automated thematic and frequency analysis
Evidano runs unsupervised theme discovery across all transcripts to surface recurring barriers such as food taboos and fuel/time constraints. Frequency and co-occurrence networks help prioritise actionable themes tied to IYCF core indicators.
Cross-segment comparison & trend tracking
Evidano compares themes by role (AWW vs ASHA vs mother), by cluster (44 AWCs), and by time (baseline vs month 6 vs endline) to reveal shifts. The platform can generate difference-in-differences style summaries for qualitative outcomes, showing changes in counselling language and reported acceptability.
Deliver stakeholder-ready outputs
Evidano exports hierarchical codebooks, subcodes, illustrative quotes grouped by theme, co-occurrence graphs and word clouds that are ready for policy briefs. A collaborative AI chat over the corpus lets program managers ask sourced questions such as 'Which villages reported fuel shortages as primary barrier in Oct–Dec 2024? ' and get instant, traceable answers.
Security & compliance
Evidano encrypts data in transit and at rest, and Evidano models are proprietary and customer data is not used to train third-party models, which is crucial for sensitive child-health data.
Two-week workflow: from field audio to findings
This two-week workflow converts field audio and notes into findings in 14 days using a structured checklist teams can reproduce for the Palghar study. The checklist covers upload, transcription, theme generation, codebook approval, cross-segment analysis, visuals, and a stakeholder brief.
- Day 0–2: Upload transcripts, audio, diet-card spreadsheets; set language and custom dictionary for local tribal terms.
- Day 3–4: Auto-transcribe (if needed), run batch translation review and redaction for consented PII.
- Day 5–7: Auto-generate initial themes; review and approve a draft codebook; import existing codebook from prior Palghar work if available.
- Day 8–10: Tag segments for roles and clusters; run cross-segment frequency and co-occurrence analysis.
- Day 11–12: Produce visuals (hierarchical codes, co-occurrence network, word clouds) and extract top 20 illustrative quotes per theme.
- Day 13–14: Create a one-page stakeholder brief and run an AI Q&A session over the project to prepare presentation notes for policymakers.
Practical implications for program teams and evaluators
Program teams and evaluators should tie qualitative themes directly to measurable IYCF indicators (MDD, MMF, MAD, timely CF) and use AI-enabled synthesis to reduce time-to-insight and make cross-cluster comparisons reproducible. Applying weekly process logs helps detect implementation drift early and target corrective training.
- Use weekly process logs to detect early implementation drift and design corrective refresher trainings.
- Leverage cross-segment quote banks to build locally resonant IEC materials (pictorial recipe captions, common objection scripts).
- Document and version your codebook so future scale-up across other tribal blocks (as the protocol suggests) remains consistent.
FAQ: Qualitative analysis of IYCF practices
What counts as qualitative evidence for IYCF outcomes?
Qualitative evidence includes IDIs with frontline workers, mothers’ group discussions, supervisor logs, and behavioral observations that explain why a measurable indicator changed or did not change. These sources provide explanatory context for changes in MDD, MMF, MAD, and timely CF.
How do you compare themes across two timepoints?
Compare themes across two timepoints using a consistent codebook mapping combined with frequency and prominence measures and co-occurrence shifts. Evidano automates the comparisons and highlights statistically meaningful shifts in language and topics.
Can AI handle local language terms and low-literacy inputs?
Yes, AI can handle local language terms and low-literacy inputs when you add a custom dictionary and supply sample translations. Evidano supports transcription and translation with custom dictionaries to preserve cultural nuance.
How quickly can teams produce stakeholder-ready findings?
Teams can produce stakeholder-ready findings in about two weeks using the compact workflow described here, starting with one IDI batch and two weeks of process logs for a pilot.
Wrapping up & next steps
The Palghar IYCF protocol (published 15 July 2026) is a clear example of mixed-methods field work that benefits from reproducible, AI-assisted qualitative workflows. If your team needs to transform transcripts, monitoring logs and diet-card notes into policy-ready themes and cross-segment comparisons, consider a compact Evidano pilot.
- Start with a 2-week pilot: import one IDI batch plus two weeks of process logs and produce a thematic brief and visuals.
- See how much time thematic coding and cross-cluster comparisons save, then scale to full project analysis.
- Request a demo and upload a sample corpus at Evidano to get a tailored pilot plan.