Qualitative analysis of training interventions

Evidano is an AI-powered qualitative data analysis platform that automates transcription, bilingual ingestion, AI-assisted coding, and cross-segment synthesis for implementation research.

A Jan–Sep 2024 multilevel onsite training plus a 6-month mentorship in Northwest Ethiopia raised provider knowledge to a median of about 90.9 and reduced the diagnostic interval by 54.2% (56.5 → 25.8 days).

• The intervention reached 1, 020 HEWs, 29 primary-level and 18 level-1 clinicians through onsite and cascade trainings (Jan–Sep 2024).
• Provider knowledge scores rose to a median ~90.9 after training, measured by KAP surveys with 100% response.
• The diagnostic interval fell 54.2% (from 56.5 to 25.8 days) based on a chart review of n=100 patients.
• A 2-week Evidano pilot can ingest transcripts, import a CFIR codebook, run AI-assisted coding, and produce stakeholder-ready memos with visuals.

The evaluation used a quasi-experimental pre–post mixed-methods design from January–September 2024 across primary, secondary and tertiary tiers. The intervention combined immersive onsite workshops (10 days for level-1 clinicians, 7 days for primary providers, 5 days pictorial modules for HEWs) with a 6-month mentorship program (monthly onsite and remote supervision).

• Quantitative methods: KAP surveys normalized 0–100, chart review of 100 pediatric oncology records, pre/post comparison.
• Qualitative methods: 18 in-depth interviews (IDIs) with Level I, 29 Level II interviews, multiple HEW focus group discussions; analysis framed by CFIR.
• Limitations: no contemporaneous control group; conflict and seasonal factors required adaptive delivery such as cascade mini-trainings.

A 2-week pilot can ingest source documents, run AI-assisted coding, and deliver stakeholder-ready evidence tables and visuals.

• Step 1: Gather inputs: workshop slide decks, KAP spreadsheets, audio of IDIs/FGDs, n=100 chart extracts, and referral registers.
• Step 2: Ingest uploads to Evidano; enable a custom dictionary for local terms (for example, local words for lymphadenopathy).
• Step 3: Auto-transcribe and translate; review low-confidence segments flagged by Evidano; redact PII automatically.
• Step 4: Import a CFIR codebook or create one in Evidano; run AI-assisted coding and validate on a 10% sample.
• Step 5: Generate thematic frequency, co-occurrence network, and cross-segment comparisons (HEW vs facility vs region).
• Step 6: Produce a stakeholder memo with evidence tables, top quotes, and visual exports for program managers.
• Step 7: Iterate and use Evidano chat over your corpus to answer ad-hoc questions, for example, “Which facilities reported reagent shortages in July 2024? ”

If you are preparing to synthesize a mixed-methods implementation study like the Gondar training project, link CFIR themes to measurable process changes such as KAP, referrals, and diagnostic intervals.

• Use AI to remove transcription bottlenecks, standardize coding, and produce cross-segment evidence tables that decision-makers trust.
• Try a 2-week Evidano pilot to ingest transcripts, import your codebook, and produce a stakeholder memo with visuals, Try Evidano for free.