The primary keyword for this post is "qualitative analysis of leadership programmes." According to the PLOS One article (Klinga et al., 2026), a six-month person-centred leadership programme admitted 80 leaders in 2022 and produced qualitative interviews with 13 participants between 27/01/2023 and 16/03/2023. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the PLOS One article (Klinga et al., 2026), interviews lasted 40–65 minutes with a mean of 52 minutes and the study was published on 22 July 2026. This post explains how AI-enabled qualitative research practices can reproduce, scale, and interrogate the PLOS One findings and turn interview data into actionable programme evaluation outputs.
Key Takeaways
According to the PLOS One study (Klinga et al., 2026) PLOS One, a six-month educational programme supported development across three leadership tracks: self-leadership, team-leadership, and workforce development.
- 1) According to PLOS One, 80 leaders were admitted to the programme in 2022 and 13 leaders were interviewed between 27/01/2023 and 16/03/2023.
- 2) According to PLOS One, interviews lasted 40–65 minutes (mean 52 minutes) and the study was published on 22 July 2026.
- 3) According to PLOS One, participants reported three change visions summarized as “Live as you learn, ” “Strive for equal relations, ” and “Enable co-creation.”
- 4) A participant told the researchers, “You get the chance to pause and reflect in a way that you don’t usually do, ” (Interview 10, PLOS One).
What happened in the PLOS One study and how it was measured
According to the PLOS One article (Klinga et al., 2026), the programme was a six-month course offered to members of the Swedish Association of Health Professionals and admitted 80 leaders in 2022.
According to the PLOS One article (Klinga et al., 2026), the researchers purposefully sampled 13 programme completers and conducted digital individual interviews between 27/01/2023 and 16/03/2023 to capture lived leadership experiences.
According to the PLOS One article (Klinga et al., 2026), interviews were transcribed verbatim and analysed using conventional content analysis with an interpretative step that linked categories to three leadership concepts: self-leadership, team-leadership, and workforce development.
According to the PLOS One article (Klinga et al., 2026), credibility and dependability were supported through co-coding, peer debriefing, reflexivity, and reporting in line with COREQ standards.
Findings snapshot table
| Date | Metric | Value | Implication (as reported) |
|---|---|---|---|
| 2022 | Programme admissions | 80 leaders | Large cohort context for workplace-based assignments (PLOS One) |
| 27/01/2023–16/03/2023 | Interviews conducted | 13 leaders | Purposeful heterogeneous sample for in-depth qualitative insight (PLOS One) |
| Interviews (reported) | Duration | 40–65 min, mean 52 min | Sufficient interview length for rich narratives (PLOS One) |
| 22 July 2026 | Publication date | Published in PLOS One | Peer-reviewed dissemination of qualitative evaluation (PLOS One) |
Implications for qualitative researchers and programme evaluators
According to PLOS One (Klinga et al., 2026), programme evaluation should combine workplace assignments with reflection and peer support to produce observable shifts in leadership language and behaviour.
According to PLOS One (Klinga et al., 2026), evaluators should capture multi-level preconditions (manager support, time for coaching, and methods for co-creation) because those preconditions influenced whether participants could sustain changes after the programme.
According to PLOS One (Klinga et al., 2026), researchers should triangulate leader self-reports with employee and patient perspectives to measure real-world implementation effects, because the authors note that patient and employee perspectives were not fully integrated in the current study.
How Evidano helps with qualitative analysis of leadership programmes
Problem: transcription and PII handling slows analysis
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
According to PLOS One (Klinga et al., 2026), interviews were audio recorded and transcribed verbatim; using automated transcription with custom dictionaries reduces manual time while matching the study’s need for accurate quotes.
Solution: use Evidano’s speech-to-text transcription with custom dictionaries and PII redaction to produce research-ready transcripts aligned with PLOS One methods.
Problem: coding large interview sets and tracking themes across levels
According to PLOS One (Klinga et al., 2026), the analysis linked codes to three analytical tracks: self-leadership, team-leadership, and workforce development.
Solution: Evidano supports thematic coding, hierarchical codes→subcodes, and cross-segment analyses so evaluators can reproduce the three-track framing and quantify how often themes appear across participants.
Feature link: learn more about these capabilities on Evidano features.
Problem: turning qualitative themes into stakeholder-ready metrics
According to PLOS One (Klinga et al., 2026), programmes need measurable preconditions such as managerial support and time to coach teams.
Solution: Evidano generates frequency tables, co-occurrence networks, and visual exports that translate themes like “managerial support” into counts, cross-tabs by role, and slide-ready visuals for funders and senior leaders.
Problem: iterative Q&A over transcripts and findings
According to PLOS One (Klinga et al., 2026), reflexivity and peer discussions shaped coding decisions.
Solution: Evidano’s AI chat over your documents lets teams question transcripts, pull verbatim quotes (with timestamps), and record analytic decisions for an audit trail.
FAQ: qualitative analysis of leadership programmes
How can I reproduce the PLOS One study’s three-track analysis with AI?
Answer: Reproduce the three-track analysis by aligning your codebook to the study’s analytical tracks and using AI-assisted coding to accelerate initial label assignment.
According to PLOS One (Klinga et al., 2026), the authors applied conventional content analysis and then interpreted codes through self-leadership, team-leadership, and workforce development; using AI to suggest initial codes speeds the human-led interpretative step without replacing it.
Tip: extract the PLOS One quotes and map them to code examples to calibrate coder agreement.
Can AI reliably extract participant quotations for publication?
Answer: Yes, AI platforms can reliably extract verbatim quotations when transcripts are accurate and quality-checked.
According to PLOS One (Klinga et al., 2026), quotations were translated and lightly edited for readability, so AI extraction should be paired with human verification to preserve meaning and ethical consent.
What sample sizes work for thematic leadership programme evaluations?
Answer: Small purposive samples produce depth; the PLOS One study used 13 interviews for in-depth analysis.
According to PLOS One (Klinga et al., 2026), the authors purposefully selected 13 participants and referenced Malterud et al. (2016) on information power to justify sample size, so match sampling to study aims rather than target arbitrary counts.
How should evaluators incorporate patient and employee perspectives?
Answer: Incorporate patient and employee perspectives through parallel qualitative modules and cross-compare themes.
According to PLOS One (Klinga et al., 2026), participants stressed that patient and relative involvement was necessary to validate co-creation, so include targeted interviews or surveys with those groups and use cross-segment analysis to surface alignment or gaps.
Conclusion & Next Steps
According to PLOS One (Klinga et al., 2026), person-centred leadership programmes can shift leader language and practice when workplace preconditions are present.
According to PLOS One (Klinga et al., 2026), qualitative interviews (13 participants, conducted 27/01/2023–16/03/2023) produced rich evidence that can be reproduced and scaled with AI-enabled workflows.
To replicate the PLOS One analytic approach at scale, use automated transcription, calibrated AI-assisted coding, and cross-segment visualizations to convert themes into stakeholder metrics.
Get started with a hands-on trial: Try Evidano for free.