Low enrollment from Black, Hispanic/Latinx, women, and rural populations undermines clinical research. A July 1, 2026 PLOS ONE freelisting study (n=101; data collected May–Sep 2023) mapped immediate words and sentiments around research and found shared concerns ("study, " "knowledge, " and notably "scary") plus a unique signal of "research misconduct" among Black respondents (source: PLOS ONE). This post explains how AI-enabled qualitative analysis can turn those freelist outputs into operational recruitment strategies: faster cleaning of raw lists, reproducible term-grouping (dictionaries), salience rankings, sentiment tagging, and cross-segment comparison. If you run participant recruitment, UX research, or policy evaluation, we show a practical two-week workflow you can run in this platform (www.evidano.com) to prioritize outreach and measure whether messaging reduces fear and perceived burden.
Key Takeaways
The PLOS freelisting study (n=101) found fear and perceived burden are common barriers to research participation, and prior participation shifts sentiment positively.
Evidano is an AI-powered qualitative data analysis platform that can convert freelist outputs into reproducible dictionaries, salience rankings, and visual reports in a two-week pilot.
- PLOS ONE freelisting (data May–Sep 2023) with 101 completers across Philadelphia, Atlanta, and DC metro areas identified shared and group-specific sentiments; published 1 Jul 2026.
- Only 32% of respondents had prior research experience, and those with prior experience expressed more positive views.
- Shared salient words included “study, ” “knowledge, ” “search, ” and “scary”; “research misconduct” was uniquely salient among Black respondents.
- Follow a two-week, seven-step pilot to translate freelist signals into outreach actions and measure sentiment shift.
Findings snapshot, overview sentence
This table summarizes the study date, sample, key signals, and the original source for quick reference.
Findings snapshot
| Date / metric | Value | Why it matters | Source |
|---|---|---|---|
| Published | 1 Jul 2026 | Frames the timing of analysis and recent policy context | PLOS ONE |
| Sample (completed freelisting) | n = 101 (56% Black; 23% Hispanic/Latinx; 80% women; 46% rural) | Enables cross-segment salience comparisons; many respondents belonged to multiple groups | PLOS ONE |
| Key signals | Shared: 'study', 'knowledge', 'scary'; Unique: 'research misconduct' (Black) | Targets for messaging and outreach; suggests fear and perceived burden are actionable barriers | PLOS ONE |
How the study worked (plain English), summary sentence
This section explains the freelisting instrument, data collection timeline, sample size, and analysis steps in plain language.
- Data collection: May–Sep 2023 across five health systems and one community organization, using an online freelisting instrument in Qualtrics.
- Completed freelists: 101 respondents; median age 38 (IQR 31–52); 32% previously participated in research.
- Analysis steps: participants typed lists in order; the research team grouped similar responses into parent terms, built a dictionary, and calculated Smith’s S salience indices in Visual Anthropac to identify the most important terms per group, then applied consensus sentiment tagging.
Implications for researchers, UX teams, and trial recruiters
Recruitment & trial operations
Research teams should prioritize outreach to people who have never been asked to participate because the study shows that never-asked respondents hold more negative sentiments and one positive experience increases willingness.
Address fear and effort explicitly in consent and pre-screen messaging by clarifying logistics, time commitment, and incentives.
Track the conversion funnel from initial contact to enrollment and measure sentiment shift after first contact.
UX / qualitative research teams
Freelisting reveals immediate salience but lacks context, so teams should follow up with targeted interviews or concept mapping for depth.
Standardize term groupings (dictionaries) so that related entries such as 'tests' and 'lab work' are harmonized across studies to enable reproducible cross-cohort comparisons.
Use combined salience and sentiment results to prioritize topics for probe guides or card-sorting exercises.
Policy & community engagement
The salience of 'research misconduct' among Black respondents indicates a need for long-term trust-building and transparent community partnerships.
Measure community-level change by repeating freelists at intervals after engagement campaigns to detect shifts in salient terms and sentiment.
Do more, faster with AI-assisted analysis
About Evidano
Evidano is an AI-powered qualitative data analysis platform that ingests raw lists, suggests parent-term groupings using AI, and preserves original text and provenance for auditability.
Evidano enables analysts to approve suggested groupings, export reproducible dictionaries, and run cross-segment frequency and salience-style analyses.
Problem: messy freelists → Solution in the platform
The platform addresses slow manual cleaning by ingesting raw lists, suggesting parent-term groupings, and letting analysts approve or edit groupings while preserving provenance.
This workflow reduces errors and produces an auditable dictionary for reproducible analysis.
Problem: inconsistent coding across segments → Solution in the platform
The platform applies an imported or built codebook consistently across segments using AI-assisted coding to reduce coder drift.
Consistent coding enables reliable cross-segment comparisons (for Black, Hispanic/Latinx, women, rural groups) and downstream salience-style analyses.
Problem: limited visualization for stakeholders → Solution in the platform
The platform generates visualizations such as word clouds, co-occurrence networks, and hierarchical theme-to-subcode views to show which terms cluster with 'scary' or 'research misconduct' by segment.
These visualizations help stakeholders understand which concerns are shared versus segment-specific.
Problem: multilingual inputs & PII concerns → Solution in the platform
The platform supports translation with custom dictionaries and transcription with PII redaction, and stores data encrypted without using it to train third-party models.
Data security and PII redaction are important for community trust and for ethical qualitative work.
Problem: need to collect follow-up data → Solution in the platform
The platform can deploy lightweight AI-avatar interviews to convert freelist signals into richer narratives at scale, then analyze them in the same workspace for longitudinal measurement.
This lets teams move from initial salience signals to richer follow-up data without switching tools.
Checklist: 7-step workflow to reproduce and act on freelisting results, opening sentence
This checklist is a two-week pilot to translate freelist outputs into outreach actions.
- 1) Design prompts (use the same three freelist prompts) and run a small online collection (n~100 per target area).
- 2) Import raw lists into Evidano and run automated cleaning plus suggested parent-term dictionary generation.
- 3) Approve or edit the dictionary in the workspace; export it for reproducibility and audit trail.
- 4) Auto-code for sentiment and compute term frequency and salience-like ranking across segments.
- 5) Produce visualizations: word cloud, co-occurrence, and hierarchical codes→subcodes.
- 6) Draft targeted messaging: (a) for never-asked groups, emphasize low burden and concrete logistics; (b) for Black communities, pair outreach with transparent oversight and clear benefit descriptions.
- 7) Re-run freelists or short avatar interviews after outreach to measure sentiment shift and conversion metrics.
FAQ: qualitative analysis of research participation
What did the PLOS freelisting study find about barriers to research participation?
The study found that fear and perceived burden are common barriers across groups, and prior research participation is associated with more positive sentiment.
The freelists produced shared salient terms such as 'study, ' 'knowledge, ' 'search, ' and 'scary'; 'research misconduct' was uniquely salient among Black respondents (PLOS ONE, n=101; data May–Sep 2023).
How was the freelisting data collected and analyzed?
The freelists were collected online between May and September 2023 with 101 completed freelists, and analysis used cleaning, dictionary creation, and Smith’s S salience indices in Visual Anthropac.
The research team grouped similar responses into parent terms, built a dictionary, used scree plots to select salient terms, and applied consensus sentiment tagging.
How can teams use freelisting results to improve recruitment?
Teams can use freelisting results to shape consent and pre-screen messaging by addressing fear and effort and by prioritizing contact with people who have never been asked to participate.
Follow-up steps include drafting targeted outreach, measuring initial contact → enrollment conversion, and repeating freelists after outreach to evaluate sentiment change.
How can AI-assisted tools speed reproducible qualitative analysis?
AI-assisted tools can automate cleaning, suggest parent-term groupings, and apply codebooks consistently across segments to reduce manual workload and coder variability.
Tools can also generate visualizations, support translation and PII redaction, and maintain provenance so teams can export reproducible dictionaries and reports.
Wrapping up & next steps, summary sentence
The PLOS freelisting study (n=101) gives clear, actionable signals: fear and perceived burden are common across groups, and prior participation shifts sentiment positively.
For teams that recruit, design, or measure trial equity, the most direct lever is asking more people and then measuring whether first contacts reduce fear.
Ready to operationalize? Try Evidano for free and run the seven-step pilot to share reproducible dictionaries, segment comparisons, and visual reports with stakeholders.
Ethics note: this is research-focused guidance, not clinical advice; ensure IRB review and informed consent for new participant data collection.
Topics
- qualitative analysis of research participation
- freelisting study
- research recruitment equity
- community engagement
- Evidano
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