This post explains how to apply AI-enabled qualitative research to evaluate person-centred leadership programmes, aimed at researchers, L&D teams, and health system evaluators. The primary keyword for this guide is person-centred leadership qualitative analysis, which we use to show step-by-step how an academic evaluation can be reproduced, scaled, and made auditable with AI tools. The examples come from a PLOS One evaluation published on July 22, 2026, and include exact dates, sample sizes, and participant quotes so you can extract lessons and operationalise them in your own programmes.
Key Takeaways
According to PLOS One, a six-month person-centred leadership programme delivered in 2022 was experienced by participants to influence leadership practices across self-leadership, team-leadership, and workforce development. The PLOS One study used individual interviews (n = 13) and conventional content analysis to surface three development visions: Live as you learn, Strive for equal relations, and Enable co-creation.
- 13 leaders were interviewed between 27/01/2023 and 16/03/2023, with interviews lasting a mean of 52 minutes, according to PLOS One (published 22 July 2026).
- The educational programme admitted 80 leaders in 2022 and ran six modules over six months, as reported in PLOS One.
- Participants’ leadership experience ranged from 3 to 20 years in the PLOS One sample, which shaped how they perceived change.
- The PLOS One authors emphasise that preconditions such as managerial support, time to coach teams, and workplace assignments were necessary for lasting change.
What happened and how the study measured change
The PLOS One study describes a blended six-month educational programme delivered in 2022 and evaluated with qualitative interviews in early 2023, so the dataset reflects immediate post-programme experiences.
According to PLOS One, the programme combined six modules on person-centred care and leadership, a flipped-classroom pedagogy, workplace homework assignments, peer support groups, and implementation plans; 80 leaders entered the programme in 2022 and 13 of those leaders were purposefully sampled for interviews between 27 January and 16 March 2023.
According to PLOS One, data collection used audio-recorded digital interviews (40–65 minutes, mean 52 minutes) and analysis followed conventional content analysis with an interpretative step linking categories to an overarching theme.
According to PLOS One, the evaluation focused on leaders’ self-reported changes, with three analytical tracks used by the researchers: self-leadership, team-leadership, and workforce development.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 2022 | Programme admissions | 80 leaders admitted | Provides the programme scale the PLOS One authors evaluated for recruitment and workplace spread |
| 27/01/2023–16/03/2023 | Interviews conducted | 13 leaders interviewed, mean length 52 min | Rich, in-depth accounts suitable for qualitative content analysis |
| 2022 | Programme format | Six-month blended learning with 6 modules | Blended, workplace-integrated learning supports transfer according to participants |
| 22/07/2026 | Publication date | PLOS One article published | Peer-reviewed source for replication and citation |
| Sample detail | Leadership experience range | 3–20 years | Heterogeneous experience influenced perceptions of change |
Implications for qualitative researchers and evaluators
The PLOS One results show that interview-based qualitative evaluation can surface actionable development tracks but depends on clear preconditions and workplace linkage.
According to PLOS One, evaluators should embed practical workplace assignments into programmes and time follow-up interviews 3–5 months after programme end to capture early practice change (the PLOS One interviews were 3–5 months post-programme).
According to PLOS One, include varied participants to capture transferability: the study purposively sampled leaders across roles, settings, and geography to identify common development themes despite heterogeneous contexts.
According to PLOS One, report precise operational details for reproducibility: module topics, pedagogical methods (flipped classroom), sample sizes, interview dates, and analysis approach were all specified and made the study citable and auditable.
How Evidano helps with person-centred leadership qualitative analysis
Evidano: platform definition
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Problem: Manual transcription and redaction slows analysis, as PLOS One required verbatim transcripts for conventional content analysis; Solution: Evidano’s speech-to-text features provide fast, editable transcripts with custom dictionaries and PII redaction.
Problem: Coding at scale is time-consuming for multi-module programmes; Solution: Evidano supports thematic, content, frequency, and cross-segment analyses that accelerate coding and surface co-occurrence networks and hierarchical code structures.
Problem: Multilingual or multi-site programmes need consistent terminology; Solution: Evidano’s translation tools keep custom dictionaries so you preserve domain-specific terms across transcripts.
Problem: Synthesising quotations, timelines, and preconditions from interviews is arduous; Solution: Evidano’s AI chat over your documents and visualisations (word clouds, co-occurrence networks) helps reviewers extract participant quotes like “You get the chance to pause and reflect” (Interview 10, PLOS One) and map them to themes for reporting.
Practical workflow mapping
Problem: Reproducing the PLOS One approach requires transparent audit trails; Solution: Evidano stores encrypted documents and generates an auditable trail from raw audio to final codes, which supports confirmability and dependability as described in the PLOS One trustworthiness criteria.
Problem: Detecting subgroup differences (e.g., leaders with 3 vs 20 years’ experience) is manual; Solution: Evidano’s cross-segment analyses let you compare themes, frequencies, and sentiment by role, experience, or region to replicate the PLOS One analytic tracks (self-leadership, team-leadership, workforce development).
Problem: Scaling mixed-methods evaluation needs integrated survey and interview data; Solution: Evidano ingests spreadsheets and open text so you can combine closed-ended measures with qualitative themes for richer programme evaluation.
FAQ: person-centred leadership qualitative analysis
What evidence did the PLOS One study produce about leadership change?
Answer: The PLOS One study produced qualitative evidence that participants perceived changes across three development tracks: self-leadership, team-leadership, and workforce development.
Support: According to PLOS One (published 22 July 2026), these tracks were distilled into three visions (Live as you learn, Strive for equal relations, and Enable co-creation) based on interviews with 13 leaders conducted 27/01/2023–16/03/2023.
How soon after a programme should evaluators interview participants?
Answer: Interview 3–5 months after programme completion to capture early practice changes while memories remain fresh.
Support: The PLOS One authors interviewed participants three to five months post-programme (interviews between 27 January and 16 March 2023) and reported that timing captured applied workplace activities and reflections.
How can AI speed up thematic coding without losing rigor?
Answer: AI accelerates initial coding and pattern detection while human researchers validate and refine codes to preserve trustworthiness.
Support: The PLOS One study used manual conventional content analysis with consensus coding to ensure credibility; mirroring that, Evidano’s AI can propose codes and frequencies which researchers then confirm, maintaining dependability and confirmability.
Can AI extract quotations and link them to analytic themes?
Answer: Yes, AI platforms can automatically extract verbatim quotations, tag them with speaker metadata, and associate them with analytic codes for rapid reporting.
Support: The PLOS One article quotes participants directly, for example “You get the chance to pause and reflect” (Interview 10), and an AI workflow reproducing that linkage speeds synthesis while preserving the quote provenance.
Conclusion & Next Steps
The PLOS One evaluation published on 22 July 2026 demonstrates that workplace-integrated, six-month person-centred leadership programmes can influence leader practices when preconditions are met, and that qualitative interviews (n = 13 in this study) provide the depth needed to surface development pathways.
If you run or evaluate leadership programmes, apply structured timelines (3–5 month follow-up), purposive sampling, and transparently reported analysis methods as in PLOS One to make findings usable and replicable.
To scale and accelerate trustworthy qualitative synthesis, use AI-enabled workflows for transcription, coding, cross-segment analysis, and auditable reporting.
If you want to try an AI-enabled qualitative workflow, explore Evidano’s features for transcription, analysis, and secure data handling and Try Evidano for free.