This post translates the PLOS One qualitative study on a person-centred leadership programme into actionable guidance for researchers using AI-enabled qualitative research. The primary keyword is person-centred leadership analysis, and this article explains how the PLOS One study’s methods, sample, and findings map to best practices in transcript coding, thematic synthesis, and workplace-based implementation research.
Key Takeaways
According to the PLOS One article by Klinga et al. (published July 22, 2026), a six-month blended educational programme was experienced by participants to influence leadership at three levels: self-leadership, team-leadership, and workforce development.
- 80 leaders were admitted to the programme in 2022, reported by Klinga et al. in PLOS One (2026).
- 13 leaders were purposefully interviewed between 27 January 2023 and 16 March 2023, three to five months after programme completion, with interviews lasting 40–65 minutes (mean 52 minutes), as reported in PLOS One (Klinga et al., 2026).
- When preconditions such as managerial support and time for coaching were present, participants reported measurable changes in practice across the three development tracks in July 2026, according to Klinga et al., PLOS One.
- A participant in Klinga et al., PLOS One (2026) put it plainly, “You get the chance to pause and reflect in a way that you don’t usually do” (participant, Interview 10, Klinga et al., PLOS One, July 22, 2026).
What happened and how it was measured
Answer: Klinga et al. evaluated leader experiences after a six-month educational programme using individual interviews and conventional content analysis.
According to the PLOS One article (Klinga et al., 2026), the programme combined six modules, a flipped-classroom pedagogy, workplace assignments, and workshops designed to operationalise the GPCC framework for person-centred care.
According to Klinga et al. (PLOS One, 2026), 80 leaders entered the 2022 cohort, 13 were interviewed between 27 January and 16 March 2023, and interviews were transcribed verbatim and analysed with an abductive, conventional content analysis approach to produce three analytical tracks: self-leadership, team-leadership, and workforce development.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 2022 | Programme admissions | 80 leaders admitted | Sufficient cohort size to run mixed peer groups, reported in Klinga et al., PLOS One (2026) |
| 27 Jan–16 Mar 2023 | Interviews | 13 individual interviews, mean 52 minutes | High information power for in-depth qualitative analysis, as reported in Klinga et al., PLOS One (2026) |
| July 22, 2026 | Publication | PLOS One article (Klinga et al.) published | Peer-reviewed dissemination of qualitative programme evaluation |
| Programme design | Duration & format | Six-month blended programme; 6 modules; flipped classroom | Workplace assignments + workshops supported transfer to practice, per Klinga et al., PLOS One (2026) |
Implications for qualitative researchers and evaluators
Answer: The PLOS One study demonstrates best practices for programme-focused qualitative evaluation that AI-enabled methods can accelerate without sacrificing rigor.
According to Klinga et al., PLOS One (2026), embedding assignments in workplace settings and interviewing participants three to five months post-programme produced rich behavioural accounts that captured implementation conditions and barriers.
According to the PLOS One article (Klinga et al., 2026), credible qualitative inference depended on transparent coding, negotiated consensus among multiple analysts, and careful reflexivity; AI tools should augment rather than replace these steps by streamlining transcription, code suggestion, and audit-trail generation.
How Evidano helps with person-centred leadership analysis
Problem: long transcription and manual coding cycles
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
According to Klinga et al., PLOS One (2026), the study used verbatim transcriptions and iterative coding; Evidano supports accurate automated transcription with custom dictionaries and PII redaction to speed this stage while preserving audit trails.
Use case: upload interview audio, get searchable transcripts, and export time-aligned quotes for team consensus sessions.
Problem: linking themes across levels (self, team, workforce)
Answer: Researchers need cross-segment and hierarchical code analysis to map micro changes to organisational outcomes; Evidano provides hierarchical coding, co-occurrence networks, and cross-segment frequency analysis.
According to Klinga et al., PLOS One (2026), the programme influenced leadership at three interconnected levels; Evidano’s thematic and cross-segment reports help you show which sub-categories (for example, "Reflecting on leadership" or "Power shifting") cluster with organisational preconditions in your data.
Problem: demonstrating trustworthiness and auditability
Answer: Transparent audit trails and versioned codebooks are essential for confirmability in programme evaluations.
Evidano stores coding histories, supports collaborative coding, and exports the audit trail required to follow criteria like those used by the PLOS One authors (credibility, dependability, confirmability), and features are documented on the Evidano features page.
FAQ: person-centred leadership analysis
How can AI accelerate qualitative analysis of leadership programmes?
Answer: AI accelerates time-consuming tasks like transcription, initial code suggestions, and co-occurrence mapping while leaving interpretive synthesis to human researchers.
According to Klinga et al., PLOS One (2026), rigorous qualitative inference required human reflexivity and negotiated consensus; AI is most effective when paired with researcher-led interpretation and peer debriefing.
When should interviews be scheduled after a leadership programme?
Answer: Schedule interviews three to five months after programme completion to capture applied practice, as used in the PLOS One study.
According to Klinga et al., PLOS One (2026), interviews conducted three to five months post-programme produced actionable accounts of behaviour change and preconditions for sustained practice change.
What sample sizes work for in-depth programme evaluation?
Answer: Purposeful sampling with information power is appropriate; small samples can be sufficient when interviews are rich and analytically focused.
According to Klinga et al., PLOS One (2026), 13 purposefully selected interviews provided adequate depth for conventional content analysis because participants varied across role, setting, and experience.
How do you show trustworthiness in AI-assisted qualitative work?
Answer: Combine AI outputs with documented human coding decisions, reflexive memos, and inter-coder consensus procedures.
According to Klinga et al., PLOS One (2026), trustworthiness was strengthened by collaborative coding, reflexivity logs, and an audit trail; Evidano’s collaborative workspace and exportable audit logs support these practices.
Conclusion & Next Steps
Klinga et al.’s PLOS One study (published July 22, 2026) shows that a six-month, blended person-centred leadership programme can produce self-reported changes in self-leadership, team-leadership, and workforce development when organisational preconditions are met.
For qualitative researchers, the study underscores the value of workplace-embedded assignments, delayed interviews (three to five months), and transparent analytic procedures that AI can accelerate without replacing human interpretation.
If you evaluate leadership programmes and want to compress transcription-to-insights time while keeping auditability, consider tools that combine accurate speech-to-text, hierarchical coding, and collaborative audit trails.
Get started with an AI workflow designed for qualitative researchers, or Try Evidano for free.