Qualitative analysis of EIR perceptions in Bangladesh

Evidano is an AI-powered qualitative data analysis platform that ingests transcripts, survey sheets, and EIR CSVs, supports PII redaction and auto-coding, and produces stakeholder-ready reports.

The Rajshahi City PLOS One pilot (data Jul–Sep 2024; published 2 July 2026) found broadly positive caregiver and health worker perceptions, while providers reported operational barriers such as slow connectivity and device issues.

• Sample: n = 321 total (305 caregivers, 16 health care providers), cross-sectional perceptions using 5-point Likert plus open-ended questions.
• Operational metrics reported in the pilot include 87% facility-based registration and approximately 88% timely registration within 45 days.
• Caregiver satisfaction ranged roughly 59–78% satisfied and 16–22% highly satisfied; HCPs reported 75–88% highly satisfied, interpret HCP figures cautiously because n=16.
• This analysis is descriptive, so combine thematic counts with registry metrics and targeted follow-ups before making causal program decisions.

This PLOS One pilot assessed feasibility and acceptability of an EIR in Rajshahi City, caregivers and health workers reported mostly satisfied or highly satisfied responses, and common provider challenges were slow connectivity and device issues.

Read the source: PLOS One.

• Study period: July–September 2024; Published: July 2, 2026.
• Sample: n = 321 (305 caregivers, 16 HCPs).
• Design: cross-sectional, Likert plus open-ended questions; descriptive analysis only.

The study used a pretested semi-structured questionnaire with 5-point Likert items and open-ended prompts to measure feasibility and acceptability among caregivers and health care providers.

Quantitative items were summarized descriptively in SPSS, and open-ended responses were reviewed and narratively summarized but not shared publicly because of potential identifiers.

• Focus: perceptions of EIR-supported services (reminders, certificates, access to information), not an effectiveness trial.
• Limitations: purposive sampling at EPI sessions, cross-sectional design, small HCP sample (n=16), self-report bias.

This checklist lists seven reproducible steps to convert open-ended feedback and EIR exports into actionable recommendations.

Step 1: Collate datasets, transcripts, open-text survey fields, and EIR CSV exports; ensure unique keys (child ID or household).

Step 2: Redact PII and map local terms via a custom dictionary before upload.

Step 3: Auto-code with Evidano to generate initial themes; review and refine the codebook (human-in-the-loop).

Step 4: Run cross-segment frequency analysis (caregiver vs HCP; facility vs outreach) to quantify theme prevalence.

Step 5: Link themes to registry metrics (timeliness, missed dose counts) to prioritize operational fixes.

Step 6: Produce stakeholder-ready visuals (top themes, quote bank, co-occurrence network) and a one-page decision memo.

Step 7: Design a two-week follow-up using AI avatar interviews for quota validation or targeted probes.

The Rajshahi pilot provides descriptive perceptions but does not establish causal impact, so combine thematic counts with registry metrics and targeted follow-ups to inform decisions.

• Ready to reproduce this workflow on your EIR data? Explore a secure, research-focused pipeline at Evidano or Try Evidano for free and spin up a pilot to import transcripts, code open text, and produce stakeholder-ready reports in days.
• Ethics note: qualitative findings in health contexts are research-focused and non-diagnostic, obtain ethical approvals and anonymize data before analysis.