This post explains how researchers can use AI-enabled qualitative research workflows to turn interview data about disability services into actionable findings. The primary keyword, qualitative analysis of disability services, frames a practical summary of a PLOS One study that interviewed 12 adults with chronic illnesses and official disability reports and was published on 28 July 2026. Read on for exact sample numbers, dates, verbatim participant quotes, and reproducible steps you can use in your next study.
Key Takeaways
According to the PLOS One article, people with chronic illnesses experience disability across education, employment, social participation, and healthcare, yet formal entitlement does not guarantee effective service use. PLOS One.
- 12 adults were interviewed between 9 July 2024 and 7 January 2025, according to the PLOS One study (Polat et al., published 28 July 2026).
- 70% of participants were women and 50% had type 1 diabetes, as reported in PLOS One on 28 July 2026.
- Half of participants (6 of 12) reported limited or no awareness of disability services in the PLOS One interviews conducted between 9 July 2024 and 7 January 2025.
- Three themes emerged in PLOS One (Impact on Daily Life, Awareness of Disability Services, Accessibility of Disability Services), highlighting informational and structural barriers to utilisation.
What happened and how the study was done
The PLOS One study interviewed 12 adults with chronic diseases who held official disability reports in Türkiye between 9 July 2024 and 7 January 2025 to explore lived experience and service expectations.
According to Polat et al. in PLOS One, data were collected via semi-structured, in-depth interviews lasting 30 to 45 minutes and analyzed with inductive content analysis within a phenomenological perspective.
According to the PLOS One article, purposive plus snowball sampling was used, all approached individuals agreed to participate, and coding decisions were documented to create themes and an audit trail.
Findings Snapshot
| Date / Source | Metric | Value | Implication |
|---|---|---|---|
| 9 July 2024–7 January 2025 (PLOS One) | Interviews conducted | 12 participants | Small qualitative sample, in-depth accounts for thematic insight |
| Published 28 July 2026 (PLOS One) | Gender distribution | 70% female | Women were overrepresented, consider targeted recruitment |
| Published 28 July 2026 (PLOS One) | Primary condition | 50% had type 1 diabetes | Diabetes-specific service gaps, e.g., pumps and supplies, were highlighted |
| Published 28 July 2026 (PLOS One) | Service awareness | 50% reported limited/no awareness | Information deficits are a clear intervention point |
| Published 28 July 2026 (PLOS One) | Main themes | 3 themes (Impact, Awareness, Accessibility) | Use these themes as codebook starters for related datasets |
Implications for qualitative researchers
Researchers should treat formal disability certification as a starting point, not proof of service access: the PLOS One study found certification did not ensure utilisation.
- Design: According to PLOS One (published 28 July 2026), purposive sampling plus snowballing produced rich narratives but limited transferability; consider purposive quota sampling for broader subgroup comparison.
- Interviewing: Polat et al. used 30–45 minute semi-structured interviews; researchers should prepare probes on education, employment, social participation, and healthcare to surface service-awareness gaps.
- Analysis: The PLOS One article used inductive content analysis to derive three themes; start with those themes as initial codes and apply cross-segment analysis to compare subgroups (for example, diabetes vs non-diabetes).
- Ethics note: The PLOS One authors restricted raw data for re-identification risks; researchers must plan access controls and ethical anonymization for transcripts and audio.
How Evidano helps translate interviews into evidence
Definition and overview
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano supports transcription, translation, thematic coding, frequency and cross-segment analysis, and visualizations that map codes to subcodes, all of which accelerate the workflow Polat et al. used manually in PLOS One.
Problem: manual transcription and PII risks → Solution: automated secure transcription
Polat et al. audio-recorded interviews and transcribed them verbatim; Evidano automates transcription with custom dictionaries and PII redaction to match that workflow and reduce manual effort.
Learn more about transcription features on the Evidano speech-to-text page.
Problem: slow coding and limited comparators → Solution: thematic + cross-segment analysis
The PLOS One study reported three high-level themes; Evidano accelerates theme emergence using AI-assisted coding, code co-occurrence networks, and cross-segment comparison to test whether themes differ by condition, gender, or employment status.
See the product features for automated thematic and cross-segment tools.
Problem: presenting findings to stakeholders → Solution: extractable quotes and visual exports
Polat et al. supported themes with verbatim participant quotes such as "It greatly affected my education. During that period, I was disabled, so I was always falling behind in my classes" (Participant P6), which Evidano preserves and links to coded segments for transparent reporting.
Evidano exports codebooks, frequency tables, and visualizations for reports or policy briefs.
Problem: ethics and data governance → Solution: encrypted storage and access controls
The PLOS One authors restricted raw data for ethical reasons; Evidano offers encrypted storage and role-based access to support similar confidentiality requirements and secure sharing with ethics committees.
FAQ: qualitative analysis of disability services
How many interviews are enough to study disability experiences?
Answer: Depth and saturation, not large N, determine sample sufficiency in qualitative disability research.
Supporting detail: The PLOS One study used 12 interviews collected between 9 July 2024 and 7 January 2025 and reported that data saturation was reached; the authors cited standard qualitative guidance that small purposive samples can be sufficient when interviews produce rich, confirming patterns.
What themes should researchers expect when studying chronic-disease-related disability?
Answer: Expect themes around daily-life impact, service awareness, and accessibility.
Supporting detail: Polat et al. (PLOS One, published 28 July 2026) identified three themes (Impact on Daily Life, Awareness of Disability Services, and Accessibility of Disability Services) that are practical starting points for codebooks.
Can AI tools reproduce the kind of inductive analysis used in this PLOS One study?
Answer: AI-assisted tools can rapidly surface candidate themes and link verbatim quotes but should be combined with researcher-led interpretation.
Supporting detail: The PLOS One study used manual inductive content analysis; AI-enabled qualitative platforms can accelerate coding, produce co-occurrence maps, and enable cross-segment queries while leaving final interpretation to researchers.
How should researchers handle verbatim quotes and participant confidentiality?
Answer: Quote selection should anonymize identifiers and be approved by ethics protocols.
Supporting detail: Polat et al. included participant quotes like "I cannot participate in activities that require physical exertion; I have to move more slowly" (Participant P9), and the authors restricted raw datasets for ethical reasons; follow similar anonymization and access controls.
Conclusion & Next Steps
The PLOS One study (Polat et al., published 28 July 2026) shows that chronic disease-related disability affects multiple life domains and that certification does not guarantee service awareness or access.
Researchers and program teams should prioritize information dissemination, integrated service pathways, and disability-sensitive data collection to measure impact over time.
If you want to automate transcription, accelerate inductive coding, and produce reproducible cross-segment analyses from interview datasets like those in PLOS One, try an AI-enabled platform that preserves confidentiality and audit trails.