Qualitative analysis of chlamydia subfertility

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).

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.

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.

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.

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