Low and uneven clinical research enrollment harms validity and equity. This PLOS One study (published July 1, 2026) used freelisting to surface what underrepresented groups think about research, finding a mix of neutral, positive, and durable negative terms (n = 101, May–Sep 2023). If you run recruitment, UX, or community-engaged studies, this post shows how to translate those freelisting outputs into prioritized actions using reproducible qualitative analysis workflows and AI-enabled tools.
Key Takeaways
The PLOS One freelisting study (published July 1, 2026) shows that fear and unfamiliarity are clear barriers to research participation and that simply asking people matters.
- The study surveyed n = 101 completed freelists (May–Sep 2023) and found 'scary' was the top cross-group salient term.
- More than half of respondents had never been asked to join research, and those never asked showed more negative sentiment.
- A unique signal of 'research misconduct' appeared among Black respondents, pointing to targeted trust-building needs.
- Two high-leverage interventions are proactive invitations and fear-reducing outreach, supported by reproducible freelist analysis workflows.
Findings snapshot
| Metric | Value | Notes / Source |
|---|---|---|
| Publication date | July 1, 2026 | PLOS One |
| Data collection | May–September 2023 | Web-based freelisting survey |
| Sample (completed freelists) | n = 101 | 57 Black (56%), 23 Hispanic/Latinx (23%), 81 women (80%), 46 rural (46%) |
| Prior research experience | 32% previously participated; 58% never asked | Those never asked yielded more negative salient terms |
| Top cross-group salient term | 'scary' | Appeared across prompts and groups |
| Unique signal | 'research misconduct' (Black participants) | Potential historical/contemporary mistrust signal |
Fast take: what changed and why it matters
This section answers the question: why the PLOS One freelisting study matters for recruitment and representativeness.
The freelisting study highlights reproducible signals of fear and unfamiliarity in underrepresented groups and shows that proactive asking is a simple, high-impact intervention.
- Why researchers should care: small, targeted changes in messaging and initial outreach may shift attitudes and improve representativeness.
- What you’ll learn: how to convert freelist text into salience, sentiment, and segment comparisons faster and reproducibly with AI-enabled qualitative workflows.
How the freelisting study worked (plain English)
This section explains how the study captured and analyzed freelist responses.
Researchers recruited adults in Philadelphia, Atlanta, and Washington, DC metro areas via clinic flyers and a community partner, screened for eligibility, obtained consent, and asked three freelisting prompts about 'research', being asked to participate, and being a study participant.
- Technique: freelisting captures top-of-mind terms and allows frequency and order to inform importance (salience).
- Analysis steps used by the authors: dictionary creation, grouping synonyms, salience indices (Smith’s S), scree plots to find the 'elbow', and manual sentiment categorization.
- Limitations noted by the authors: web mode priming, regional sample, and small n for intersectional subgroup analysis.
What the qualitative analysis research participation reveals for teams
For recruitment & community engagement teams
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'.
For UX / qualitative researchers
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).
For policy & clinical teams
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.
Do more, faster with Evidano
From freelist text to prioritized actions
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.
Visualize and validate
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.
Collect follow-ups and protect data
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.
Two-week pilot: run the authors’ insights in Evidano
This section gives a compact, reproducible two-week pilot plan to validate the PLOS freelisting signals in your context.
Follow this step-by-step pilot to import, analyze, and prototype outreach messages using freelist data.
- Day 0–2: Import existing freelist spreadsheet or survey exports (or transcribe recorded interviews) into Evidano; map synonyms with the auto-suggest dictionary.
- Day 3–5: Auto-code with a draft codebook (fear, burden, trust, benefit, logistics); run frequency and cross-segment comparisons (for example, prior participants vs never asked).
- Day 6–9: Produce co-occurrence networks and top quotes for each salient theme; flag group-unique signals such as 'research misconduct'.
- Day 10–14: Prototype two outreach messages (one addressing fear; one offering invitation plus low-burden participation) and export shareable visual briefs for community partners or IRBs.
FAQ: qualitative analysis research participation
Q: When should I use freelisting versus interviews?
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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'.
Q: How do I compare segments reliably?
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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.
Q: Is freelisting ethical for sensitive topics?
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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.
Wrapping up: your next two moves
This section gives two concrete next steps to act on the study’s diagnostic.
Operationalize the study’s insight that fear and unfamiliarity are recruitment barriers by prioritizing proactive invitations and fear-reducing messaging, and automate cleaning, salience, and cross-segment comparison with an AI-enabled qualitative platform.
- Run the two-week pilot above or bring your freelist or survey data into Evidano for instant thematic, frequency, and cross-segment analyses.
- Start here: review the paper at PLOS One and then Try Evidano for free to apply these workflows to your corpus.
Topics
- freelisting
- qualitative analysis
- research recruitment
- salience
- community engagement
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