TL; DR: A July 1, 2026 PLoS ONE study used web-based freelisting (May–Sep 2023, n=101) to map attitudes toward clinical research among understudied groups and found persistent fear and burden signals, especially among people never previously asked to join studies. This post explains how researchers and UX teams can turn those freelists into actionable, segment-level intelligence using AI-enabled qualitative analysis, and how a secure AI-enabled analysis platform accelerates the process while keeping data secure.
Key Takeaways
Freelisting in the PLoS ONE study identified top-of-mind terms such as "scary", "study", "knowledge", and "effort", and people never previously asked to participate showed more negative sentiment.
Freelisting plus AI-enabled qualitative analysis turns open-text lists into prioritized, segment-aware actions for recruitment and messaging.
- PLoS ONE study published July 1, 2026, used web-based freelisting (May–Sep 2023; n=101).
- Key numeric signals reported: 56% Black, 23% Hispanic/Latinx, 80% women, 46% rural; 32% prior research experience; 59 (58%) had never been asked to participate.
- High-leverage actions: prioritize first-contact outreach and explicitly address fear and perceived burden in consent and messaging.
Fast take + source
Fast take: Freelisting captured top-of-mind terms across Black, Hispanic/Latinx, women, and rural respondents, repeatedly surfacing terms like "scary", "study", "knowledge", and "effort".
- Paper: Published July 1, 2026 (PLoS ONE).
- Data window: May–September 2023; completed freelists n=101.
- Key numeric signals: 56% Black, 23% Hispanic/Latinx, 80% women, 46% rural; 32% had prior research experience; 59 (58%) had never been asked to participate.
Findings snapshot
| Metric | Value | Source / Note |
|---|---|---|
| Sample size (freelisting) | 101 | PLoS ONE; data collected May–Sep 2023 |
| Median age (IQR) | 38 (31–52) | Reported in study demographics |
| Major shared salient terms | “study”, “knowledge”, “search”, “scary” | Salience indices from freelist responses |
| Unique flags | “Research misconduct” (Black respondents) | Potential historical / contemporary mistrust signal |
| Prior experience effect | Prior participants → more positive sentiment | Those never asked showed more negative salient terms |
How the study worked (plain English)
The study used three freelist prompts, cleaned responses into parent terms, and computed Smith's S salience scores in Visual Anthropac to identify salient items.
- Method in brief: Participants answered three freelist prompts on (1) the word “research”, (2) feelings when asked to participate, and (3) being a participant. Responses were cleaned into parent terms, then imported into Visual Anthropac to compute Smith's S salience scores. The team used scree plots to choose the elbow and labeled top items as salient.
- Recruitment: Flyers, QR codes, clinics and one community organization across Philadelphia, Atlanta, and Washington DC metros.
- Language: English and Spanish versions; professional translation used.
- Limitations noted by authors: web-based freelisting may prime responses, sample limited geographically and by healthcare access.
Do more, faster with Evidano
Evidano definition
Evidano is an AI-powered qualitative data analysis platform that ingests open-text freelists and survey exports, groups terms, computes salience-like and frequency metrics, and preserves provenance for auditability.
From freelists to prioritized themes: Manual cleaning and salience calculation is slow and error-prone, while Evidano ingests open-text freelists and spreadsheets, applies term grouping via custom dictionaries, computes frequency and salience-like metrics, and produces ranked thematic lists in minutes.
Compare segments reliably
Evidano enables reliable cross-segment analysis by automating repeated coding tasks and producing quantitative contrasts.
Problem: Cross-segment comparisons (e.g., prior vs. never asked) require repeated manual coding.
Evidano: run cross-segment analyses (frequency, sentiment, co-occurrence) so you can quantify which concerns are universal vs. segment-specific and export tables/plots for stakeholders.
Preserve nuance and auditability
Evidano preserves source provenance and exemplar quotes so cleaning never loses context.
Problem: Cleaning loses provenance and exemplars.
Evidano: retain clickable source quotes, export hierarchical codes→subcodes, and attach dictionaries used for grouping so every theme is auditable for IRB and community partners.
Run small pilots and scale
Evidano supports quick pilots that ingest transcripts or open survey responses and produce iterative thematic reports.
Problem: Teams need quick pilots to test messaging or recruitment scripts.
Evidano: run a 2-week pilot (ingest transcripts or open survey responses), produce thematic + frequency + cross-segment reports, and iterate on outreach wording using the AI chat over your corpus.
Security & ethics
Evidano encrypts data, supports PII redaction and custom dictionaries, and does not use customer data to train third-party models, making it suitable for research-grade pipelines.
Problem: Sensitive respondent data and confidentiality are essential.
Evidano: data is encrypted, supports PII redaction and custom dictionaries, and is never used to train third-party models, suitable for research-grade pipelines.
FAQ: qualitative analysis of research participation
What did the PLoS ONE freelisting study find?
The study found that top-of-mind terms included “scary”, “study”, “knowledge”, and “effort”, with people never previously asked showing more negative sentiment.
The PLoS ONE paper (published July 1, 2026) reports freelist salience across Black, Hispanic/Latinx, women, and rural respondents and notes unique flags such as “research misconduct” among Black respondents.
How did the freelisting method identify salient terms?
The freelisting method captured immediate words from respondents, cleaned them into parent terms, and used Visual Anthropac to compute Smith's S salience scores to rank items.
Participants answered three prompts and the team chose top items using scree plots to identify the elbow, labeling those above the elbow as salient.
What should researchers change based on these findings?
Researchers should prioritize first-contact outreach and explicitly address fear and perceived burden in messaging and consent flows.
Actions recommended include low-barrier first contacts, community liaisons, transparent descriptions of risks and benefits, and supports for time and transportation.
How can teams replicate freelisting analysis at scale?
Teams can replicate freelisting analysis at scale by ingesting raw freelist CSVs, applying cleaning dictionaries, computing frequency and salience-like metrics, and running cross-segment contrasts.
The blog outlines a seven-step workflow to ingest data, auto-group synonyms, produce scree-style plots, tag sentiment, contrast segments, and generate stakeholder-ready visuals.
So what for researchers, UX teams, and trial designers, implications
Prioritize first-contact outreach
Researchers should treat the first ask as a high-leverage conversion point because people never previously asked showed more negative sentiment.
Finding: People never previously asked to participate showed more negative sentiment.
Action: design low-barrier, high-trust first contacts (community liaisons, opt-in registries) and treat the first ask as a conversion point.
Address fear and perceived burden directly
Teams should address fear and perceived burden directly because terms like “scary”, “effort”, and “lab rats” were salient across groups.
Finding: “Scary”, “effort”, and “lab rats” were salient across groups.
Action: craft messaging and consent flows that demystify procedures, transparently describe risks/benefits, and highlight time/transportation supports.
Segment, don’t assume
Designers should segment outreach because some sentiments were uniquely salient to specific groups, such as “research misconduct” for Black respondents.
Finding: Many sentiments overlapped across groups, but some (e.g., “research misconduct”) were uniquely salient to Black respondents.
Action: pair broad structural fixes (flexible visits, incentives) with tailored cultural responses and community accountability measures.
Quick workflow: replicate freelisting insights with AI (7 steps)
This section lists a seven-step workflow to replicate freelisting insights using AI.
1) Ingest raw freelist CSVs and audio transcripts into Evidano (or upload survey exports).
2) Apply or import the study’s cleaning dictionary, auto-group synonyms into parent terms.
3) Run frequency + salience-like ordering and produce scree-style plots to identify the elbow.
4) Tag sentiment at term-level and validate with a small sample of coder checks.
5) Run cross-segment contrast (e.g., prior participants vs. never asked) and export ranked differences.
6) Generate stakeholder-ready visuals: term co-occurrence networks, hierarchical code trees, and exemplar quote lists.
7) Iterate: feed revised outreach copy into a small cohort pilot, re-run the pipeline, and measure change in sentiment.
Ethics & research note
This blog refracts published research for methods and workflow guidance and is not clinical or diagnostic advice.
As with the original study, these are research-focused insights and not clinical or diagnostic recommendations. Ensure informed consent, local IRB approval, and culturally-appropriate community engagement when collecting or reusing participant data.
Wrapping up & next steps
The PLoS ONE freelisting study (July 1, 2026; n=101) shows that first-contact outreach and explicit fear and burden mitigation are high-leverage actions to improve representativeness.
- Next move 1: If you run recruitment or community-engaged research, import recent open-text responses into an AI-enabled qualitative analysis platform and run a cross-segment salience and sentiment report.
- Next move 2: Use quote exports and co-occurrence networks to design consent copy and outreach scripts that address the “scary” signal directly.
Ready to try it? Start a pilot with your freelist or survey data and produce actionable, auditable findings in weeks by signing up at Try Evidano for free.
Topics
- qualitative analysis of research participation
- freelisting in research
- clinical trial recruitment
- participant sentiment
- AI qualitative analysis
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