Fast, reproducible qualitative analysis of IYCF practices is now practical for field teams. A new quasi-experimental mixed-methods protocol published 15 July 2026 outlines a 24-month intervention in Palghar, Maharashtra with 460 mother–infant (6–12 months) dyads across 44 Anganwadi Centres (plus IDIs with frontline workers) giving a rich corpus of interviews, observations and structured forms you can convert to decision-ready evidence (source: PLOS ONE). In this post you’ll get a step-by-step workflow for AI-enabled qualitative analysis of IYCF practices and concrete ways Evidano speeds transcription, thematic coding, cross-segment comparisons and stakeholder reports so you can move from field notes to program decisions faster.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that enables teams to extract themes, illustrative quotes and cross-segment comparisons from mixed-method IYCF datasets faster and with consistent coding. This workflow applies directly to the Palghar protocol, a 24-month mixed-methods study published 15 July 2026 with 460 baseline and 460 endline mother–infant dyads across 44 Anganwadi Centres.
- The Palghar protocol yields a mixed corpus (audio IDIs, diet cards, registers, structured questionnaires) ideal for AI-assisted thematic and frequency analysis.
- Evidano ingests audio, video, PDFs, spreadsheets and images, supports custom dictionaries for local terms and PII redaction, and automates code-suggestion with human review.
- A reproducible 7-step workflow converts field recordings to validated insights and one-page decision memos linking themes to MDD/MMF/MAD outcomes.
Fast take: why this study matters for qualitative teams
The Palghar protocol combines baseline and endline surveys (n=460 each), weekly process indicators, and in-depth interviews with AWWs/ASHAs to test a socio-behavioural SBCC plus hot-meal intervention over 12 months, published 15 July 2026. The mixed corpus (audio IDIs, diet cards, monitoring registers and structured questionnaires) is exactly the kind of multi-format dataset where AI-assisted qualitative analysis delivers time and consistency gains.
- Primary payoff: extract themes, illustrative quotes, and segment comparisons (for example, by AWC, tribal subgroup, or compliance level) without manual re-reading of hundreds of pages.
- Ethics note: this is research-focused analysis, results are not diagnostic; maintain informed consent and de-identify sensitive data before automated processing.
Snapshot: study at a glance
This table summarizes the Palghar study dates, sample and core intervention components for quick reference.
Snapshot: study at a glance
| Date / Item | Metric | Value | Source / Note |
|---|---|---|---|
| Published | Article date | 15 July 2026 | PLOS ONE |
| Sample | Mother–infant dyads | 460 baseline; 460 endline | Cluster sampling across 44 AWCs |
| Coverage | Anganwadi Centres | 44 (in Ganjad PHC) / 77 total AWCs in PHC area | Study framework and site maps |
| Design | Duration & phases | 24 months (6m prep, 12m intervention, 6m post) | Registration CTRI/2024/06/068427 (06 Jun 2024) |
| Intervention | Core components | SBCC training, pictorial IEC, hot cooked complementary feeds, micronutrients | Process indicators weekly; anthropometry and clinical checks monthly |
What the protocol collected (and why it’s ideal for AI-assisted analysis)
This section lists the data types the protocol collected and explains their suitability for automated thematic and content-frequency analysis. Data types: semi-structured IDIs with frontline workers, structured questionnaires for mothers using eight WHO IYCF indicators, anthropometric readings, diet cards, weekly registers, photos/videos from WhatsApp groups and IEC materials. This mix yields both structured metrics and rich free-text suitable for thematic and content-frequency analysis.
- Qualitative data will be transcribed verbatim and analysed thematically (Nvivo cited in protocol).
- Quantitative data (MDD, MMF, MAD, initiation of complementary feeding) allow cross-segment testing (pre versus post, by AWC, by tribal subgroup).
- Process logs (weekly) provide time-series signals of adherence to intervention components.
So what for researchers and implementers?
Program evaluators
Program evaluators should use thematic coding to surface implementation barriers, for example meal uptake and cultural food restrictions, and link those barriers to process metrics to identify which AWCs need additional training.
Program evaluators should prioritize outcome linkage by mapping themes such as 'hot meal acceptability' and 'recipe feasibility' to changes in MDD and MMF across clusters.
UX / Behavior-change teams
UX and behavior-change teams should run fast-scan code co-occurrence networks to show which messages (pictorial, demo, role play) align with caregiver recall and behavior.
UX and behavior-change teams should segment quotes by literacy, tribe and AWC to tailor IEC materials and recipe booklets.
Policy & health analysts
Policy and health analysts should aggregate qualitative themes into succinct policy briefs that quantify frequency, for example '25/44 AWCs reported supply disruption', and illustrate them with verbatim quotes.
Policy and health analysts should estimate scale-up readiness using combined thematic and process indicators rather than single anecdotal signals.
Do more, faster with Evidano: qualitative analysis of IYCF practices
Ingest multilingual field material
Evidano ingests audio, video, PDFs, spreadsheets and images (diet cards, IEC), and supports transcription with custom dictionaries for local terms and PII redaction, critical for tribal languages and culturally specific food names.
Automate coding and keep human oversight
Evidano imports an existing codebook or generates one from initial interviews, applies thematic coding across transcripts, returns suggested codes for review, and builds hierarchical themes to subcodes for consistent cross-rater reliability.
Cross-segment and frequency analysis
Evidano runs cross-tab analyses, for example theme prevalence by AWC, by tribal subgroup, or by compliance level, and surfaces high-impact contrasts such as which SBCC elements correlate with MDD gains.
Visualize evidence for stakeholders
Evidano generates word clouds, co-occurrence networks and hierarchical code trees plus downloadable quote lists for policy briefs and presentations, eliminating manual slide-building.
Follow-up and fill gaps
Evidano supports AI avatar interviewers to collect quick follow-ups, for example two-week recipe adoption checks, and feeds results back into the same project for longitudinal thematic tracking.
Security & compliance
Evidano stores data encrypted end-to-end; uses proprietary LLMs tuned for qualitative research; customer data is never used to train third-party models, important for handling vulnerable tribal participant data.
7-step workflow: from field recordings to action-ready insights
This 7-step workflow reproduces the Palghar-style mixed-methods synthesis using AI support. Follow these steps to reproduce the Palghar-style mixed-methods synthesis with AI support:
- 1) Collect and label: centralize audio, photos of diet cards, registers and survey spreadsheets by AWC and date.
- 2) Transcribe + translate: use transcription with custom dictionary for local food and tribe terms and auto PII redaction.
- 3) Preliminary coding: auto-suggest codebook from 10–15 seed IDIs, then refine with two coders.
- 4) Batch-code corpus: apply codes across transcripts and tag illustrative quotes for each theme.
- 5) Cross-segment analysis: run frequency and comparative tests (pre versus post, by AWC, tribal subgroup).
- 6) Visualize & validate: export co-occurrence maps and quote decks; validate with frontline staff (member-check).
- 7) Report & iterate: produce a one-page decision memo mapping top three barriers to actionable fixes (training, supply logistics, recipe adaptation).
FAQ: qualitative analysis of IYCF practices
Q: Can AI handle local food names and tribal terms?
A: Yes, use custom dictionaries at transcription and translation stages to preserve semantic accuracy and avoid misclassification during coding.
Q: How do I compare themes quantitatively?
A: Convert theme presence into binary or frequency variables and run cross-tabs or regression alongside MDD, MMF and MAD metrics to test associations.
Q: Is automated analysis ethical with vulnerable populations?
A: Only process de-identified data with informed consent; keep human review in the loop for interpretation and publish de-identified aggregates, not raw quotes that could re-identify participants.
Wrapping up: your next two moves
This closing section gives immediate next steps for teams preparing to analyse a mixed-methods IYCF study like the Palghar protocol. If you are preparing to analyse a mixed-methods IYCF study like the Palghar protocol, start by centralizing transcripts and structured forms, then run a seed-code extraction on a subset to calibrate AI-assisted coding.
- Pilot a two-week ingest of 10 IDIs and diet cards into Evidano to see automated themes, cross-segment tables and presentation-ready quote decks: Try Evidano for free.
- Original study: PLOS ONE. For WHO IYCF core indicators see WHO.