This post shows how to apply AI-enabled qualitative research methods to a six-month person-centred leadership programme evaluation. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. The primary keyword for this post is person-centred leadership programme evaluation, aimed at qualitative researchers, learning-and-development teams, and health system evaluators who need reproducible thematic synthesis from interview transcripts.
Key Takeaways
According to the PLOS One article, the six-month educational programme was experienced to influence leaders’ practices across self-leadership, team-leadership, and workforce development (PLOS One). The PLOS One study interviewed 13 leaders selected from a cohort of 80 admitted to the programme in 2022, and the interviews were conducted between 27 January 2023 and 16 March 2023.
- 13 leaders were interviewed for the study, as reported in PLOS One, with interviews lasting a mean of 52 minutes (interviews held 27 January–16 March 2023).
- The programme under study ran for six months and admitted 80 leaders in 2022, according to PLOS One (published 22 July 2026).
- When preconditions such as managerial support and workplace time were present, participants reported changes in self-leadership, team-leadership, and workforce development in July 2026, as described in PLOS One.
- Representative participant feedback included: “You get the chance to pause and reflect in a way that you don’t usually do” (participant, Interview 10, quoted in PLOS One).
What Happened and how the study measured change
The PLOS One study evaluated a blended, six-month educational programme delivered in 2022 and described in the article published 22 July 2026 by Klinga et al. The study targeted Swedish health and social care leaders and used purposive sampling to select 13 participants from the 80 programme admits in 2022 for in-depth interviews.
According to PLOS One, data were collected via individual digital interviews conducted between 27 January 2023 and 16 March 2023, each lasting 40–65 minutes (mean 52 minutes). The authors analysed transcribed interviews using conventional content analysis and then interpreted categories across three analytical tracks: self-leadership, team-leadership, and workforce development.
According to PLOS One, the study produced an overarching thematic structure with three visions, labelled Live as you learn, Strive for equal relations, and Enable co-creation, which were further divided into nine sub-categories describing actionable leadership behaviors.
Direct quotations in PLOS One illustrate participant experience, for example: "It’s about using words without defining what we mean by those words" (participant, Interview 5, quoted in PLOS One).
Findings Snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 2022 | Programme admits | 80 leaders | Cohort size for selection and workplace-based assignments |
| 27 Jan–16 Mar 2023 | Interviews conducted | 13 leaders, mean 52 minutes | Primary qualitative dataset for thematic analysis |
| 2026-07-22 | Article published | PLOS One article by Klinga et al. | Peer-reviewed dissemination of evaluation findings |
| 2022 (programme duration) | Programme length | Six months, six modules | Blended learning with workplace assignments to support transfer |
| Participant profile | Leadership experience | 3–20 years | Heterogeneous sample across levels and settings |
Implications for researchers and learning designers
The PLOS One study shows that embedding leadership learning in workplace practice is essential to change behaviour. According to PLOS One, practical assignments, peer reflection, and managerial support were named by participants as preconditions for sustaining change.
According to PLOS One, evaluation plans should include follow-up measures beyond self-report: the authors call for further research measuring impacts from employees, patients, and organisational metrics over time (PLOS One, published 22 July 2026).
- Design programme evaluation to capture multi-level outcomes: the PLOS One study used leader interviews but recommends collecting employee and patient perspectives (PLOS One).
- Include workplace-based assignments and peer groups, because participants in the PLOS One study reported that these elements supported translation into practice.
- Measure contextual preconditions such as time for coaching and senior-manager support, because PLOS One identified those factors as necessary for continuous development.
How Evidano Helps
Problem: interviews are rich but slow to synthesize
Answer: Use AI-assisted thematic and frequency analysis to accelerate synthesis while preserving traceability to source quotes.
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. For a programme like the one described in PLOS One, Evidano can ingest interview transcripts, tag quotations, and produce coded themes aligned to the three analytical tracks (self-leadership, team-leadership, workforce development). See the platform capability overview at the Evidano features page Evidano features.
Problem: maintaining link between claims and quotes
Answer: Keep direct quote provenance and exportable audit trails to meet qualitative trustworthiness standards.
Evidano preserves source metadata and allows export of quote→code mappings to support dependability and confirmability as described by PLOS One. Use Evidano’s transcription and PII redaction features if you record interviews, see Evidano speech-to-text.
Problem: cross-segment comparisons are manual and error-prone
Answer: Use automated cross-segment and frequency analyses to detect patterns by role, region, or years of experience.
Evidano can compute cross-segment theme frequencies and visualize co-occurrence networks so you can quantify claims such as those in PLOS One (for example, which sub-categories were most frequentemente reported by leaders with 3–7 years of experience). For security and compliance questions, see Evidano data security.
FAQ: person-centred leadership programme evaluation
What evidence did the PLOS One study provide that leadership programmes change practice?
Answer: The PLOS One study provides qualitative self-reports from 13 leaders that describe perceived changes across self-leadership, team-leadership, and workforce development.
According to PLOS One, those 13 interviews (selected from 80 admits in 2022) yielded three high-level visions and nine sub-categories showing how participants translated person-centred ethics into leadership actions, but the authors recommend additional data sources to measure objective change over time.
How were interview data analysed in the PLOS One study?
Answer: The PLOS One study used conventional content analysis followed by interpretative clustering into three analytical tracks.
According to PLOS One, coding was performed iteratively, with the first and last authors jointly negotiating categories and two additional authors providing peer debriefing to enhance credibility.
Can AI help reproduce a PLOS One style thematic analysis?
Answer: Yes, AI-assisted tools can speed up coding and thematic clustering while preserving an audit trail for qualitative validity.
Practically, AI can propose preliminary codes, group similar meaning units, and surface representative quotations for each theme; researchers should then validate and refine AI outputs to meet the trustworthiness criteria used in PLOS One.
What additional data would strengthen a programme evaluation like the PLOS One study?
Answer: Triangulate leader interviews with employee and patient feedback and objective organisational metrics over time.
The authors of the PLOS One article explicitly recommend collecting employee and patient perspectives and conducting longitudinal follow-up to assess sustained practice change.
Conclusion & Next Steps
The PLOS One evaluation (published 22 July 2026) shows that a six-month person-centred leadership programme can be experienced to change leaders’ practices when workplace preconditions are met.
For qualitative researchers and L&D teams, the study provides a replicable coding frame (self-leadership, team-leadership, workforce development) and concrete design cues such as workplace assignments and peer reflection.
If you want to reproduce and scale this type of analysis, use AI-enabled tools to speed coding, retain quote provenance, and produce cross-segment metrics; Evidano supports those workflows and secure data handling.
Next step: Try Evidano for free to import transcripts, run thematic and cross-segment analyses, and produce audit-ready exports for publication or internal evaluation.