Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the PLOS One study PLOS One, a six-month educational programme on person-centred leadership was offered in 2022 and produced self-reported changes among participants. This post explains, for qualitative researchers and program evaluators, how to extract reproducible insights from interview-based evaluations like the PLOS One study using AI-enabled qualitative research methods and tools.
Key Takeaways
According to the PLOS One article PLOS One, a six-month person-centred leadership programme (2022) was experienced by participants to support development in three linked tracks: self-leadership, team-leadership, and workforce development.
- 80 leaders were admitted to the programme in 2022, as reported in the PLOS One study published on July 22, 2026.
- 13 participants were interviewed between 27 January 2023 and 16 March 2023, and the interviews lasted 40–65 minutes with a mean of 52 minutes, according to PLOS One.
- The PLOS One authors concluded on July 22, 2026 that the programme worked best when organisational preconditions (manager support, time to coach, empowering methods) were present.
What happened: Study design and measurements
According to PLOS One, researchers evaluated leaders’ experiences after a six-month educational programme delivered in 2022 and reported in the article published on July 22, 2026.
According to PLOS One, 80 leaders were admitted to the programme in 2022 and 13 purposefully sampled leaders were interviewed by the research team between 27 January 2023 and 16 March 2023, which was three to five months after the programme ended.
According to PLOS One, data collection consisted of audio-recorded digital interviews transcribed verbatim and analysed with conventional content analysis, using three analytical tracks: self-leadership, team-leadership, and workforce development.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 2022 | Programme admissions | 80 leaders admitted | Sizable cohort context for implementation activities, reported by PLOS One |
| 27 Jan–16 Mar 2023 | Interviews | 13 leaders; mean length 52 min | Qualitative sample reached information power for exploratory analysis, according to PLOS One |
| 2022 | Programme length | Six months | Blended, workplace-based learning supports transfer to practice, according to PLOS One |
| 22 Jul 2026 | Publication date | PLOS One article published | Peer-reviewed evidence for person-centred leadership programme outcomes |
Implications for qualitative researchers and program evaluators
Answer: Use structured, reproducible coding plus workplace-linked evidence to assess behavioural shifts.
According to PLOS One, interview themes clustered under three visions (Live as you learn, Strive for equal relations, and Enable co-creation) illustrating how to map micro (self), meso (team), and macro (workforce) changes in a single coding frame.
According to PLOS One, embedding practical workplace assignments and reflective exercises was a key mechanism, which suggests evaluators should capture both self-reported change and observable practice-level evidence when possible.
How Evidano helps: From interview audio to program insights
Problem: Slow transcription and inconsistent coding
Solution: Use Evidano for fast, accurate transcription and consistent thematic coding.
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents and provides transcription with custom dictionaries and PII redaction; this addresses the PLOS One workflow where interviews were audio-recorded and transcribed, as described by PLOS One.
Problem: Hard-to-reproduce thematic synthesis across cases
Solution: Combine AI-assisted coding with frequency and cross-segment analyses to surface patterns and exceptions.
Evidano supports thematic, content, frequency, and cross-segment analyses and visualisations, which lets teams reproduce the three-track interpretation (self-leadership, team-leadership, workforce development) that PLOS One used to organise findings; see Evidano features.
Problem: Stakeholder reporting and evidence translation
Solution: Generate extractable quotes, coded exemplars, and implementation-ready summaries for leaders and patients.
Evidano’s AI chat over your documents and exportable visualisations make it easier to translate qualitative findings into actionable recommendations for managers, a precondition that PLOS One identified as necessary for sustained change.
Problem: Privacy and governance for interview data
Solution: Use Evidano’s secure pipelines and transcription options to respect ethical approvals.
Evidano provides transcription and enterprise data controls; for teams working under ethical restrictions like those in the PLOS One study, Evidano’s approach supports encrypted storage and governance consistent with research requirements; see Evidano data security.
FAQ: qualitative analysis of person-centred leadership
How can AI help analyze interview data like in the PLOS One study?
AI can accelerate transcription, initial coding, and pattern detection while preserving human interpretive control.
According to PLOS One, interviews were transcribed verbatim and analysed with content analysis, and AI tools can reproduce those steps faster by producing searchable transcripts, suggesting candidate codes, and highlighting co-occurrence patterns for human reviewers.
Which metrics should evaluators extract from leadership programme interviews?
Extract: participant counts, timing (when interviews occurred), code frequencies, and exemplar quotations.
According to PLOS One, useful metrics included cohort size (80 admitted in 2022), interview timing (Jan–Mar 2023), and theme prevalence across the three tracks; pairing these metrics with quotes clarifies both prevalence and meaning.
How do I preserve trustworthiness when using AI for qualitative coding?
Preserve trustworthiness by combining automated suggestions with independent human coding and audit trails.
According to PLOS One, the authors established credibility via negotiated consensus and an audit trail; AI workflows should replicate that transparency by storing versioned codebooks, reviewer notes, and code-to-text links for auditability.
Can AI identify implementation preconditions reported in qualitative data?
Yes, AI can surface preconditions by clustering co-occurring codes about context, resources, and barriers.
According to PLOS One, preconditions such as continuous manager support, time to coach, and empowering working methods were critical, and AI-assisted co-occurrence analysis can help quantify how often those preconditions appear alongside reported changes.
Conclusion & Next Steps
According to PLOS One, the six-month 2022 leadership programme supported leaders’ self-leadership, team-leadership, and workforce development when organisational preconditions were present.
For qualitative researchers and evaluators, using AI-enabled tools speeds transcription, standardises coding, and produces extractable evidence that stakeholders can act on.
If you want to trial these methods and process interview datasets like the PLOS One study, Try Evidano for free.