Boost Recall: Qualitative Analysis of Clinician Validation

Evidano is an AI-powered qualitative data analysis platform that helps scale transcript ingestion, coding, and item-level analyses for reproducible evaluation of clinician behaviours.

Recalling a validating consultation versus an invalidating one correlated with higher incidental recall of health messages, about 18.5% greater odds in the published study.

• The published study (July 20, 2026) analysed n = 245 adults with chronic pain and found ~18.5% higher odds of recalling each message after recalling a validating consultation.
• Item-level modelling (binomial GLMM) and an automated keyword algorithm (≈97.5% agreement with manual scoring) were central to measuring recall.
• Teams can reproduce similar analyses by combining audio/transcript ingestion, AI-assisted coding for validation behaviours, and exporting item × participant tables for GLMMs.

Fast take: Recalling a validating versus an invalidating clinical consultation raised the odds of incidental recall of short health messages by approximately 18.5% in Lee et al.'s study published July 20, 2026.

Read the original paper at PLOS ONE.

• Population: adults with chronic pain (≥3 months); data collected 8–30 Oct 2024.
• Design: quasi-random autobiographical memory task → audio of 20 health messages → incidental recall test.
• Primary outcome: item-level recall scored via a validated keyword algorithm; analysis with binomial GLMM.

Study design: Participants recalled either a validating or an invalidating past consultation, then listened to 20 short health tips and completed an incidental recall task.

Participants on Prolific reported a validating or invalidating past consultation and either wrote about a validating consultation or an invalidating one for at least 3 minutes, then listened to 20 randomised short health messages and completed an incidental recall test.

• Why item-level modelling matters: item-level outcomes capture variability across messages and participants and match incident recall outcomes.
• Quality controls: attention checks, AI-generated text screening (12 flagged responses removed), and outlier removal left 245 datasets.
• Key nuance: the observed effect was direct (validation → better recall), not mediated by changes in pain-related fear in this study.

This two-week pilot reproduces a validation→recall analysis on a small corpus and produces an item × participant table for mixed-effects modelling.

• 1) Collect or upload 30–100 consultation transcripts or audio clips; include a simple post-encounter recall test (text/audio).
• 2) Auto-transcribe in Evidano, add custom dictionary entries for local clinical terms.
• 3) Create a validation vs invalidation codebook (examples + anchors) and run AI-assisted coding; review 10–20% manually.
• 4) Extract quotes and compute frequency and co-occurrence; merge with recall scores or short surveys.
• 5) Export a clean table (item × participant) for a mixed-effects model in R or use Evidano’s cross-segment analysis to compare groups.
• 6) Produce a short brief: observed effect size, example quotes, and recommended clinician behaviours to test in a controlled pilot.

When working with clinical transcripts ensure consent, de-identification, and local ethics approvals because the cited study used autobiographical recall and was non-diagnostic.

This study used autobiographical recall and was non-diagnostic. When working with clinical transcripts, ensure consent, de-identification, and local ethics approvals; Evidano supports PII redaction to help with compliance.

Conclusion: The PLOS ONE study (July 20, 2026) provides a replicable signal that perceived clinician validation correlates with better recall of health information, approximately 18.5% higher odds.

If your goal is to quantify how communication behaviours affect retention, combine rigorous item-level outcomes with AI-enabled qualitative pipelines to scale insights without losing rigour.

Ready to test this on your transcripts or consultation recordings? Try Evidano for free.