AI-enabled qualitative analysis of IYCF interventions

Tribal Palghar’s new IYCF protocol was published 15 July 2026 and defines a 24-month mixed-methods intervention across 44 Anganwadi Centres and approximately 460 mother–infant dyads. For qualitative teams this study produces hundreds of IDI transcripts, diet-cards, WhatsApp threads and field notes to code, triangulate and present to stakeholders. This post shows how AI-enabled qualitative analysis turns that mass of unstructured text into thematic, frequency and cross-segment insight, and ties a short workflow to the PLoS One protocol while noting ethics: clinical decisions should rely on local clinical teams and protocol approvals.

Key Takeaways

Evidano is an AI-powered qualitative data analysis platform that accelerates thematic coding, cross-segment comparisons, and audit-ready reporting for IYCF interventions like the Palghar study. The Palghar PLoS One protocol covers 460 mother–infant dyads across 44 Anganwadi Centres and produces hundreds of IDI transcripts, weekly process registers, diet-cards and WhatsApp exchanges ideal for AI-assisted synthesis.

The Palghar study is a 24-month quasi-experimental mixed-methods protocol published 15 July 2026 with CTRI registration dated 06 June 2024, producing a large longitudinal qualitative corpus.
Prepare for local-language variability: the protocol lists multiple tribal groups so custom dictionaries and bilingual reviewers are essential during transcription.
Evidano workflows can ingest audio, scan diet-cards and import WhatsApp exports, run transcription with custom glossaries and PII redaction, and produce exportable codebooks and timestamped audit trails.
Cross-segment analysis (AWW vs ASHA vs mothers, by AWC, baseline vs endline) links qualitative themes to quantitative IYCF indicators (MDD, MMF, MAD) for faster, targeted program decisions.

Fast take: what the PLoS protocol means for qualitative teams

The PLoS One protocol defines a 24-month quasi-experimental mixed-methods study in Ganjad PHC, Palghar District, Maharashtra, enrolling 460 infants aged 6–12 months across 44 Anganwadi Centres and producing baseline, intervention and post-intervention qualitative data. The protocol timeline is baseline → 12-month intervention → post-intervention (total 24 months) and is registered as CTRI/2024/06/068427.

Why it matters: Expect hundreds of IDI transcripts (Feb–Sep 2024 situational analysis), weekly process registers, diet-cards and WhatsApp exchanges, all qualitative data that drive interpretation of outcomes (MDD, MMF, MAD).
Payoff: Use AI to speed coding, surface local feeding norms and generate cross-segment comparisons (for example, AWW vs ASHA vs mothers) ahead of endline reporting.
Original protocol: PLoS One.

Findings snapshot

Date / Item	Metric	Value	Source	Implication
Publication	Article published	15 July 2026	PLoS One	Protocol available to replicate qualitative methods
Trial registry	CTRI registration	06 June 2024 (CTRI/2024/06/068427)	Protocol	Pre-registered design improves credibility
Sample	Mother–infant dyads (pre & post)	460 per survey; 44 AWCs	Protocol	Large field corpus for thematic analysis
Phases	Study duration	24 months (6m prep, 12m intervention, 6m post)	Protocol	Longitudinal process data and weekly registers
Qual methods	IDI window	Feb–Sep 2024	Protocol	Multiple frontline worker perspectives to code

Study design & methods (plain English)

The study combines structured surveys and anthropometry with qualitative in-depth interviews (IDIs) of Anganwadi Workers (AWWs), ASHAs, supervisors and mothers. Intervention components are SBCC training for frontline workers and mothers, pictorial IEC (flipcharts, videos, recipe booklets), provision of hot cooked complementary feeds at Anganwadi Centres, and iron, calcium and vitamin D supplementation.

Qualitative pipeline in the protocol: IDIs → verbatim transcription → NVivo v13 thematic analysis → code/theme development → triangulation with quantitative indicators.
Ethics & consent: Institutional approval was received from ICMR-NIRRCH Ethics Committee (approval Nov 13, 2023); written informed consent is obtained for parents with special procedures for illiterate participants.

So what for researchers and program teams?

For qualitative researchers

Qualitative researchers should expect high variability in language and local terms across the listed tribal groups and plan for custom dictionaries and manual validation during transcription.

Prioritize early codebook harmonization: the protocol uses COREQ and TREND checklists, and teams should map codes to IYCF core indicators (MDD, MMF, MAD).

For implementation / program managers

Implementation and program managers should treat process data (weekly registers, diet-cards, WhatsApp) as primary sources for fidelity checks and turn these into dashboards to spot drops in feed provision or training decay.

Use cross-segment comparisons (by AWC, by supervisor, by village) to target refresher trainings where adherence lags.

For policy & evaluation analysts

Policy and evaluation analysts should use qualitative themes to explain the reasons behind changes in MDD/MMF/MAD and capture local food availability and cultural restrictions to inform scalability within ICDS/POSHAN frameworks.

Document the protocol’s pretest and feasibility steps when assessing replicability and cost-effectiveness.

Do more, faster with Evidano (mapped to this use case)

Ingest & clean

Ingest recorded IDIs, diet-card scans, WhatsApp exports and survey spreadsheets into Evidano for transcription and automated PII redaction. Evidano transcribes audio with custom dictionary support for local tribal terms and redacts PII automatically.

Code, theme & quantify

Apply automated thematic clustering and frequency counts, then import or apply the protocol’s codebook in Evidano to generate hierarchical code→subcode maps and co-occurrence networks. Evidano’s outputs help identify which barriers (for example, food taboos) co-occur with low MDD.

Cross-segment analysis

Compare themes by segment (AWW vs ASHA vs mothers, by AWC, baseline vs endline) using Evidano’s cross-tab visualizations that link qualitative themes to quantitative outcomes such as MDD improvements.

Collaborate & report

Export stakeholder-ready visuals, searchable quote banks and an audit trail for reproducibility from Evidano, and use the AI chat to answer ad-hoc queries over your dataset (for example, “show quotations about hot-cooked meals from AWWs”).

Security & governance

Evidano uses encrypted storage and proprietary LLMs tuned for qualitative research, and customer data is not used to train third-party models, supporting the protocol’s emphasis on participant confidentiality.

Checklist: reproduce this study’s qualitative synthesis in 7 steps

This checklist lists seven reproducible steps you can run in a single Evidano workflow to mirror the study’s qualitative synthesis.

1) Import audio, field notes, diet-card scans and WhatsApp exports for the 44 AWCs.
2) Run transcription with a custom dictionary for local tribal terms and PII redaction.
3) Auto-generate initial codes and frequency lists; review and refine the codebook and apply COREQ alignment.
4) Tag segments by role (AWW, ASHA, mother) and by timepoint (baseline/intervention/post).
5) Produce co-occurrence networks to find linked barriers (for example, meal frequency vs caregiver beliefs).
6) Triangulate themes with quantitative indicators (MDD/MMF/MAD) and create a short policy brief.
7) Export quotes, visuals and an audit trail for publication and stakeholder meetings.

FAQ: AI-enabled qualitative analysis of IYCF interventions

How do I handle local language and low literacy?

Handle local language and low literacy by using custom dictionaries and bilingual reviewers during transcription. Evidano supports translation with custom glossary entries to preserve culturally specific terms.

Can AI preserve auditability for publication?

Yes, AI platforms can preserve auditability through exportable codebooks, flagged reviewer changes and timestamped audit trails that support COREQ/TREND-aligned reporting. Evidano enables these exports to support transparency in methods.

Is the platform secure for vulnerable populations?

Secure handling of sensitive data requires PII redaction, access controls and encrypted storage, paired with your IRB-approved consent and data management plan. Evidano implements encrypted storage and data governance controls as described in this post.

Topics

IYCF qualitative analysis
AI qualitative analysis
Palghar IYCF
Anganwadi Centres data
MDD MMF MAD

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AI-enabled qualitative analysis of IYCF interventions

Key Takeaways

Fast take: what the PLoS protocol means for qualitative teams

Findings snapshot

Study design & methods (plain English)

So what for researchers and program teams?

For qualitative researchers

For implementation / program managers

For policy & evaluation analysts

Do more, faster with Evidano (mapped to this use case)

Ingest & clean

Code, theme & quantify

Cross-segment analysis

Collaborate & report

Security & governance

Checklist: reproduce this study’s qualitative synthesis in 7 steps

FAQ: AI-enabled qualitative analysis of IYCF interventions

How do I handle local language and low literacy?

Can AI preserve auditability for publication?

Is the platform secure for vulnerable populations?

Wrapping up: next steps

Topics

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