Researchers and program teams evaluating STI interventions need rapid, reproducible ways to turn mixed-methods data into policy-ready evidence. This post refracts a June 18, 2026 PLOS ONE study of young adults’ views on a hypothetical chlamydia-related subfertility test (focus groups n=19; survey n=426, median age 22) through the lens of AI-enabled qualitative analysis. You’ll get a concise readout of the study’s methods and numbers, practical implications for qualitative workflows, and a 7-step reproducible plan to run the same analyses in a reproducible analysis platform. Important: predictive risk tools are clinical-adjacent: this guidance is research-focused and non-diagnostic.
Key Takeaways
The June 18, 2026 PLOS ONE study found that 78% of surveyed young adults in the Netherlands would be willing to take a hypothetical chlamydia-related subfertility test, with perceived severity 75% for chlamydia and 88% for subfertility. Researchers should plan for clear communication and counseling because an increased-risk result can cause prolonged worry and behavioral misconceptions. AI-assisted, reproducible qualitative workflows can scale inductive coding, cross-segment comparison, and secure handling of sensitive transcripts.
- 78% of survey respondents (n=426) said they were willing to take the hypothetical test; the study also ran five focus groups (n=19).
- Participants perceived high severity: 75% for chlamydia and 88% for chlamydia-related subfertility.
- Sequential mixed-methods design used focus groups to build items, then an online questionnaire (Apr 1–May 15, 2024) for quantitative assessment.
- Key analytic needs include thematic coding, frequency counts, cross-segment comparisons, and careful quote selection for reporting.
- Plan for follow-up care and clear messaging to mitigate emotional downstream effects and misinformation (for example, stopping contraception).
Fast take: study snapshot
Hoenderboom et al. (Published June 18, 2026) asked young adults in the Netherlands about chlamydia-related subfertility and a potential predictive test and found high willingness to test alongside clear emotional trade-offs. Key outcomes: 78% willingness to take a test, high perceived severity (75% for chlamydia, 88% for chlamydia-related subfertility), and clear trade-offs between reassurance and mental burden.
- Read the original study: PLOS ONE
- Why it matters: mixed open-text and numeric data (focus groups + survey) reveal both behavioral intentions and communication risks, classic qualitative tasks suited to AI-assisted thematic and cross-segment analysis.
Findings snapshot
| Published | Design | Participants (n) | Median age | Willingness to test | Focus groups (n) | Notable stats | Source |
|---|---|---|---|---|---|---|---|
| 18 June 2026 | Sequential mixed-methods (focus groups → questionnaire) | 426 (survey) + 19 (focus groups) | 22 (IQR 20–24) | 78% (willing to take the potential test) | 19 | Perceived high severity: chlamydia 75%, subfertility 88% | PLOS ONE |
How the study worked (brief)
The authors ran five focus groups (n=19) to surface benefits, barriers, and test requirements, then used those themes to build and field an online questionnaire (Apr 1–May 15, 2024).
Quantitative analysis used descriptive stats and modified Poisson regression, and open-text answers were analysed with inductive content analysis (ICA).
- Qualitative input shaped closed items, a standard sequential mixed-methods pattern.
- Open-text coding: manual blind-coding of a random 100 responses, iterative theme refinement, typical but time-consuming.
- Key analytic needs from the study: thematic coding, frequency counts, cross-segment comparisons (for example, by chlamydia history), and clear quote selection for reporting.
So what for qualitative researchers and UX teams
What to watch for in similar studies
Participants expressed high demand for accuracy, many stating acceptable error rates of 1% or less, so communicate probabilistic limits clearly.
Emotional downstream effects are common: an increased-risk result can cause prolonged worry, so plan for counseling and capture affective responses in interviews.
Misconceptions are common, for example stopping contraception after an increased-risk result, so code for knowledge gaps and misinformation to inform messaging.
Design implications for survey + focus group mixes
Use early qualitative rounds to design items and validate comprehension, for example with cognitive interviewing.
Capture short open-text prompts for 'benefits' and 'barriers' and prioritize structured coding to compare across segments like age and prior infection.
Predefine what counts as a policy-actionable theme, for example 'need for follow-up care' or 'communication gap', and track frequencies.
Do more, faster with Evidano
Overview
Evidano is an AI-powered qualitative data analysis platform that ingests transcripts, surveys, and spreadsheets, applies AI-assisted thematic coding, and supports reproducible, privacy-aware workflows.
Use Evidano to scale inductive coding, produce cross-segment comparisons, and handle sensitive STI research data with research-grade privacy controls.
Ingest mixed inputs (focus groups + survey open text)
Evidano accepts transcripts, PDFs, and spreadsheets: import focus-group transcripts and the questionnaire CSV in one workspace and preserve participant metadata for cross-segment analysis.
AI-assisted thematic coding and inductive analysis
Evidano runs unsupervised theme extraction to mirror inductive content analysis, then refines with human-in-the-loop codebook import.
Evidano reports theme frequencies, representative quotes, and co-occurrence networks so you can see which barriers cluster with which demographics.
Cross-segment quant and visualization
Evidano produces frequency tables and Poisson-style comparisons by segment, for example prior chlamydia yes/no or number of partners, and generates shareable visuals such as word clouds and hierarchical code trees for stakeholder briefings.
Research-grade privacy & reproducibility
Evidano offers transcription with custom dictionaries, PII redaction, encrypted storage, and a policy that user data is never used to train third-party models, useful for sensitive STI research and ethics review.
Quick reproducible workflow (7 steps)
A compact runbook to reproduce the PLOS ONE study analysis with a reproducible qualitative workflow:
- 1) Import: upload focus-group audio (or transcripts) and the questionnaire CSV, keep demographic fields (age, prior infection).
- 2) Transcribe & clean: auto-transcribe audio with the custom dictionary, run PII redaction if required.
- 3) Seed themes: run unsupervised topic extraction on open-text to generate initial codes; review with the research team.
- 4) Human-in-loop coding: accept or merge AI codes into a codebook, then apply AI-assisted coding across the corpus for consistency.
- 5) Cross-segment analysis: compute theme prevalence by segment (for example, perceived susceptibility high vs low) and export relative risks or counts.
- 6) Visualize & validate: create co-occurrence networks and hierarchical code trees; pull representative quotes and validate with domain experts.
- 7) Deliver: generate a reproducible report and CSV exports for statistical modelling and for ethics documentation.
Wrapping up & next steps
Hoenderboom et al. give a clear example of why mixed-methods plus careful communication planning are essential when a predictive test may cause both reassurance and harm (June 18, 2026; n=426; 78% willingness).
- Try a pilot: import one focus group and 100 open-text survey responses to compare manual ICA vs AI-assisted themes and measure time saved.
- See how it works at Evidano, or request a demo to walk through a reproducible PLoS-style workflow with your data.
- Try Evidano for free