Boost Recruitment: qualitative analysis research participation

This subsection recommends actions for recruitment and community engagement teams.

Recruitment teams should prioritize proactive asking because more than half of respondents had never been asked and those never asked expressed more negative sentiment.

Recruitment materials should address fear directly by explaining procedures, time commitments, and protections to reduce responses like 'scary', 'lab rats', and 'effort'.

This subsection explains how UX and qualitative researchers should use freelisting outputs.

UX and qualitative researchers should use freelisting for fast signal detection and follow up with targeted interviews when freelisting highlights ambiguous or high-risk items, such as 'research misconduct'.

Researchers should compare segments programmatically using standardized preprocessing and salience-like composite metrics to prioritize interventions across groups (for example, prior participants versus never asked).

This subsection outlines policy and clinical implications from the freelisting signals.

Policy and clinical teams should respond to persistent mistrust signals, including the unique mention of 'research misconduct' among Black respondents, with long-term structural responses like community partnerships and transparent data use.

Operational fixes such as flexible scheduling, transportation support, and modest incentives address shared structural barriers across groups.

Evidano is an AI-powered qualitative data analysis platform that automates cleaning, parent-term grouping, salience-like rankings, and cross-segment comparisons.

Import freelisting spreadsheets and transcripts directly into Evidano to automate cleaning, grouping, and parent-term mapping, reproducing the manual dictionary step used in the paper.

Run thematic, frequency, and cross-segment analyses (for example, prior participants versus never asked) to identify terms that co-occur with 'scary' or 'misconduct'.

Use built-in sentiment tagging and custom codebook imports to standardize categories across studies and teams.

This subsection describes visualization and validation features to turn analysis into stakeholder-ready outputs.

Generate co-occurrence networks to see which concerns cluster with fear-related terms and which cluster with positive terms like 'knowledge' or 'being included'.

Export hierarchical codes, subcodes, and clickable quote lists for stakeholders to make it clear why interventions like proactive asking should be prioritized.

This subsection explains follow-up collection and data protection workflows.

Deploy scalable, short AI avatar follow-ups to ask targeted questions such as 'what made you feel scared' or 'what information would help you say yes'.

Evidano encrypts data and does not use customer data to train third-party models, addressing common privacy concerns in sensitive qualitative research.

Access the platform at Evidano.

A: Use freelisting when you need rapid, top-of-mind signals across groups and use interviews when you need deeper context.

Use freelisting for fast detection of salient terms and follow up with interviews when freelisting highlights ambiguous, emotionally charged, or high-risk items such as 'research misconduct'.

A: Compare segments reliably by standardizing preprocessing, using salience-like composite metrics, and applying the same codebook across segments.

Standardize preprocessing steps such as lowercasing and synonym mapping, compute composite salience metrics that combine frequency and order, and automate those steps to reduce analyst variance.

A: Freelisting can be ethical for sensitive topics when conducted with informed consent and appropriate privacy safeguards.

Obtain informed consent, protect data with encryption, and provide clear information to participants about use and protections before collecting freelist responses.