This post shows how to convert the PLOS One evaluation of a person-centred leadership programme into reproducible, AI-enabled qualitative research outputs for researchers and UX/organisational teams. The primary keyword for this post is qualitative analysis of person-centred leadership, and the example study is Klinga et al., PLOS One (2026). Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. Klinga et al., PLOS One (2026) evaluated a six-month educational programme delivered in 2022 and reported participant experiences across self-leadership, team-leadership, and workforce development. Klinga et al., PLOS One (2026) used individual digital interviews (13 participants, mean interview 52 minutes) collected between 27/01/2023 and 16/03/2023 to generate their findings.
Key Takeaways
According to the PLOS One study published 22 July 2026, participation in a six-month programme was experienced by leaders to develop person-centred leadership across self-leadership, team-leadership, and workforce development (PLOS One).
The study by Klinga et al., PLOS One (2026) interviewed 13 leaders in early 2023 and found practical, reflective, and relational changes when organisational preconditions were present.
- 80 leaders were admitted to the programme in 2022, according to Klinga et al., PLOS One (2026).
- 13 leaders were interviewed between 27/01/2023 and 16/03/2023, with interviews lasting 40–65 minutes and a mean of 52 minutes, Klinga et al., PLOS One (2026).
- Klinga et al., PLOS One (2026) reported three change visions: Live as you learn, Strive for equal relations, and Enable co-creation, organised across self-leadership, team-leadership, and workforce development.
- Klinga et al., PLOS One (2026) concluded on 22 July 2026 that preconditions such as managerial support and time for coaching were necessary for sustained practice change.
What happened and how the study was measured
Answer: Klinga et al., PLOS One (2026) ran a six-month blended educational programme in 2022 and evaluated participants’ experiences using qualitative interviews collected in early 2023.
Klinga et al., PLOS One (2026) recruited 80 programme participants in 2022 and purposefully sampled 13 leaders for in-depth individual interviews between 27/01/2023 and 16/03/2023 to reach information power.
Klinga et al., PLOS One (2026) analysed verbatim transcripts using conventional content analysis with an abductive step linking codes to three analytical tracks: self-leadership, team-leadership, and workforce development.
Klinga et al., PLOS One (2026) used trustworthiness measures that included joint coding by authors, peer debriefing, an audit trail, reflexivity statements, and adherence to COREQ reporting standards.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 22 July 2026 | Article published | PLOS One article by Klinga et al. | Peer-reviewed evidence of participant experiences after the programme |
| 2022 | Programme intake | 80 leaders admitted | Large cohort context for the sampled participants |
| 27/01/2023–16/03/2023 | Interviews | 13 interviews, mean 52 min | Sufficient depth to identify trajectories across three leadership levels |
| 6 months (programme length) | Programme duration | Six modules blending digital, in-person, and workplace tasks | Designed to embed PCC routines via workplace assignments |
| Post-programme (authors' conclusion) | Key themes | Live as you learn; Strive for equal relations; Enable co-creation | Framework for coding and translating to practice |
Implications for qualitative researchers and leadership teams
How should researchers validate self-reported leadership change?
Answer: Combine interview-based thematic analysis with workplace-based observations or staff/patient feedback to triangulate self-reports, Klinga et al., PLOS One (2026).
Klinga et al., PLOS One (2026) note that participants asked for patient and relative involvement to verify whether care became more person-centred, so researchers should plan mixed-source validation early.
What should evaluation timelines include for leadership programmes?
Answer: Include immediate post-programme interviews and follow-ups at 6–12 months to capture both reported behavioural change and embedded practice, Klinga et al., PLOS One (2026).
Klinga et al., PLOS One (2026) collected interviews 3–5 months post-programme and recommend further longitudinal research to measure sustained effects.
Which methods best capture team-level changes?
Answer: Use team focus groups, document analysis of meeting notes, and cross-segment coding of leader and employee transcripts, Klinga et al., PLOS One (2026).
Klinga et al., PLOS One (2026) emphasise workforce development actions such as shared decision-making and documentation routines that are visible in team artefacts.
How Evidano helps: map problems in qualitative analysis to AI-enabled features
Problem: large transcript volume, slow synthesis
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Solution: Evidano automates thematic coding, frequency counts, and co-occurrence mapping to surface the three analytical tracks described by Klinga et al., PLOS One (2026): self-leadership, team-leadership, and workforce development.
Feature link: See the platform capabilities on the Evidano features page: Evidano features.
Problem: preserving context while extracting quotes
Solution: Evidano retains source links to original transcript segments and supports export of verbatim quotations with speaker IDs and timestamps, enabling transparent audit trails as recommended by Klinga et al., PLOS One (2026).
Evidano’s tools let teams filter quotes by code, theme, or development track (for example, quotes illustrating Live as you learn).
Problem: cross-segment comparisons (roles, timepoints)
Solution: Evidano cross-tabulates themes by participant role, interview date, or project stage so researchers can compare leader self-reports with employee feedback, matching the triangulation Klinga et al., PLOS One (2026) recommend.
Evidano supports secure storage and segmented analysis so sensitive datasets may be de-identified and shared under controlled conditions, aligning with ethical rules referenced by Klinga et al., PLOS One (2026).
FAQ: qualitative analysis of person-centred leadership
How do I extract reliable themes from interviews about leadership training?
Answer: Use a systematic content-analytic workflow that combines open coding, iterative category clustering, and validation steps such as peer debriefing, Klinga et al., PLOS One (2026).
Klinga et al., PLOS One (2026) used conventional content analysis with joint coding and negotiated consensus to establish credibility.
Can AI help find the preconditions that enable change?
Answer: Yes, AI-assisted thematic analysis can surface recurring preconditions by frequency and co-occurrence of codes across participants, consistent with Klinga et al., PLOS One (2026).
Klinga et al., PLOS One (2026) emphasise managerial support and time for coaching as key preconditions; AI tools can quantify how often those preconditions appear and in which contexts.
What are quick wins to make qualitative results usable for managers?
Answer: Deliver short evidence summaries with highlighted verbatim quotations, action-oriented implications, and a small dashboard of theme frequencies, Klinga et al., PLOS One (2026).
Klinga et al., PLOS One (2026) provide example quotations such as "You get the chance to pause and reflect in a way that you don’t usually do" (Interview 10), which managers can use to justify reflective work time.
How should I handle sensitive interview data ethically?
Answer: Follow your institutional ethics approvals and de-identify transcripts before broader analysis; store raw data under controlled access as Klinga et al., PLOS One (2026) did under Swedish Ethical Review Authority constraints.
Klinga et al., PLOS One (2026) note their dataset is stored at the University of Gothenburg and subject to confidentiality checks before sharing.
Conclusion & Next Steps
Klinga et al., PLOS One (2026) show that a six-month person-centred leadership programme can shift leader reflection, team relations, and workforce practices when organisational preconditions are met.
For qualitative researchers and change teams, the pragmatic next step is to combine interview coding with cross-segment validation and longitudinal follow-up as recommended by Klinga et al., PLOS One (2026).
If you want to operationalise these workflows with AI-assisted coding, quote management, and cross-tab visualisations, try the Evidano platform for structured, secure qualitative analysis.
Get started: Try Evidano for free.