Boost Recall: qualitative analysis of patient narratives

Perceived clinician validation was associated with higher incidental recall of short audio health messages: Lee et al. (PLOS ONE, Jul 20, 2026) report ~18.5% higher odds per message in the full sample (n=245) and ~22% in a randomized subset.

Data were collected Oct 8–30, 2024; the final analysed sample was 245 after exclusions.

• Design: participants wrote about a validating or invalidating past consultation, then completed incidental recall of 20 short audio health tips.
• Effect sizes: ~18.5% higher odds per message in the final sample (n=245), ~22% in a randomized subset.
• Analysis and QC: item-level binomial GLMMs were used; 28 participants removed as outliers and 12 suspected AI-generated descriptions removed during quality control.

The paper shows that perceived clinician validation increased incidental recall of 20 audio health messages.

Lee et al. (PLOS ONE, Jul 20, 2026) tested whether autobiographical recall of validating versus invalidating consultations affected later recall of 20 audio health messages, finding participants who described a validating consultation were about 18.5% more likely to recall each message, with a robust effect of ~22% in a randomized subset.

Data collection ran Oct 8–30, 2024; the final analysed sample was 245 after exclusions, and the full paper is available at PLOS ONE.

• Design: quasi-random assignment to write about a validating or invalidating past consultation, then incidental recall of 20 short audio health tips.
• Primary outcome: item-level recall modelled with binomial GLMM; retained predictors were condition and pain duration.
• Key numbers: initial target 300, stopped at 288; 28 participants removed as outliers; 12 descriptions flagged as likely AI-generated and removed during QC.

The study used a behavioural priming task where participants wrote about a past clinical interaction they perceived as validating or invalidating.

Participants then listened to 20 short audio health tips and later wrote down whatever they recalled; recall was scored by a rule-based keyword algorithm validated against human coders with 97.5% agreement.

• Analysis used binomial GLMMs at the message-item level to preserve variability and power.
• The validation effect was direct: mediation analysis found no indirect path via measured pain-related fear or cognitive anxiety.
• Methodological notes: messages were balanced into action-oriented versus restriction-oriented, audio-delivered, and short (mean 4.25 words).

This seven-step workflow reproduces the study design as a reproducible project you can run this week.

• 1) Collect audio or text narratives post-consultation and record a short incidental recall test (20 items or fewer).
• 2) Upload audio to Evidano; run transcription with a custom medical dictionary and PII redaction.
• 3) Auto-code for validation versus invalidation using a seed codebook; review a 10% sample and refine.
• 4) Match coded themes to recall-item data (item-level rows preserve statistical power).
• 5) Run thematic frequency and cross-segment analyses (for example, by pain duration and country).
• 6) Visualise co-occurrence of validation language with emotion terms and recall outcomes.
• 7) Export a one-page stakeholder brief and CSVs for modelling (GLMM-ready).

Lee et al. (Jul 20, 2026) found perceived clinician validation was associated with better recall of health advice in people with chronic pain (n=245).

• Ready to try this on your dataset? Import transcripts, run validation coding, and link themes to outcomes in minutes with Evidano.
• Ethics note: this content is research-focused and non-diagnostic; when working with clinical populations ensure informed consent and appropriate support for participants.

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