This post explains how to extract, validate, and scale lessons from a qualitative study of person-centred leadership for researchers and UX/health teams using AI-enabled qualitative research. The primary keyword is qualitative analysis of person-centred leadership. According to the PLOS One article, a six-month educational programme offered to SAHP members in 2022 was followed up with in-depth interviews of 13 leaders, and those interviews are the basis for the study published on July 22, 2026. AI-enabled qualitative research can speed coding, surface cross-cutting themes, and quantify patterns so teams can act on leadership development evidence at scale.
Key Takeaways
According to the PLOS One study, participation in a six-month person-centred leadership programme was experienced by interviewees to influence self-leadership, team leadership, and workforce development (PLOS One).
- 13 leaders were interviewed three to five months after the programme, with interviews held between 27 January 2023 and 16 March 2023, according to PLOS One.
- The educational programme admitted 80 leaders in 2022 and ran six modules combining digital, in-person, and workplace assignments, according to PLOS One.
- The study was published on 22 July 2026 and used conventional content analysis to derive three development tracks and nine sub-categories, according to PLOS One.
What happened: study design and measurements
Answer: The study used qualitative interviews to explore leaders' experiences after an educational programme, and it measured perceived changes across self-leadership, team-leadership, and workforce development (PLOS One).
According to PLOS One, the programme was a six-month course delivered by the University of Gothenburg Centre for Person-Centred Care and the Swedish Association of Health Professionals, combining flipped-classroom lectures, workshops, reflective journals, and workplace assignments.
According to PLOS One, 80 leaders were admitted to the programme in 2022 and researchers purposefully sampled 13 participants for individual digital interviews that lasted 40 to 65 minutes (mean 52 minutes) between 27 January 2023 and 16 March 2023.
According to PLOS One, transcripts were analysed using conventional content analysis and organised into three analytical tracks: self-leadership, team-leadership, and workforce development, which the authors linked to the overarching theme Developing person-centred leadership practices.
Findings snapshot
| Date | Metric | Value (from PLOS One) | Implication for qualitative researchers |
|---|---|---|---|
| 2022 | Programme admissions | 80 leaders admitted | Large intake enables purposive sampling for diverse leadership contexts |
| 27 Jan–16 Mar 2023 | Interview window | 13 participants interviewed, mean 52 min | Short timeframe supports consistent recall but limits long-term outcome measurement |
| 22 Jul 2026 | Publication | PLOS One article published | Peer-reviewed report, useful primary source for replication or secondary analysis |
| Participant data | Leadership experience range | 3 to 20 years | Diverse experience supports transferability of themes across seniority levels |
Implications for qualitative researchers and health leaders
Answer: The study shows practical design choices and preconditions that qualitative researchers should capture when evaluating leadership programmes, according to PLOS One.
According to PLOS One, the study highlights three actionable visions (Live as you learn, Strive for equal relations, and Enable co-creation) that structure behaviour change across individual, team, and workforce levels.
Researchers should record timing (for example, this study interviewed leaders 3–5 months after programme completion), contextual support (managerial backing, coaching), and concrete workplace assignments, because PLOS One links these preconditions to whether participants could apply learning in practice.
For health leaders, PLOS One recommends involving employees, patients, and relatives early in practical workplace assignments and ensuring ongoing managerial support, because the study found that such preconditions determined the depth and sustainability of change.
How Evidano helps with qualitative analysis of person-centred leadership
Problem: interviews are rich but slow to synthesize → Solution: automated thematic + frequency analysis
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano maps interview transcripts to thematic codes, quantifies how often themes appear, and produces cross-segment comparisons so teams can see, for example, how many participants reported improved team presence versus self-reflection as reported in the PLOS One study.
For studies like the PLOS One programme evaluation, Evidano speeds the process of turning 40–65 minute interviews into replicable codebooks and visualizations, enabling researchers to reproduce the three analytical tracks (self-leadership, team-leadership, workforce development) as structured outputs.
See the Evidano features page for relevant capabilities such as thematic coding, co-occurrence networks, and cross-segment analysis.
Problem: maintaining audit trails and trustworthiness → Solution: documented analytic workflows
Evidano records coding provenance and supports collaborative review, which aligns with PLOS One’s emphasis on credibility, dependability, and an audit trail during conventional content analysis.
Evidano lets you attach memos, analytic decisions, and translator notes to codes so you can replicate the reflexive work the PLOS One authors described when two authors coded jointly and performed negotiated consensus.
Problem: scaling co-creation insights across organisations → Solution: segment and compare
Evidano lets teams compare subgroups (for example, leaders with 3–7 years experience versus 8–20 years), helping to test whether themes like 'Strive for equal relations' vary by experience as PLOS One suggests.
Evidano also supports secure data management and transcription workflows so qualitative teams can process workplace assignments and translated materials while preserving confidentiality.
FAQ: qualitative analysis of person-centred leadership
What methods did the PLOS One study use to analyze leadership change?
Answer: The study used individual interviews analysed with conventional content analysis and an interpretative step linking categories to an overarching theme, according to PLOS One.
PLOS One reports that two authors independently reviewed transcripts, codes were grouped into categories and sub-categories, and negotiated consensus and reflexivity practices were used to strengthen trustworthiness.
How soon after training were leaders interviewed in the study?
Answer: Leaders were interviewed three to five months after the programme ended, according to PLOS One.
PLOS One states that interviews took place between 27 January 2023 and 16 March 2023, which captures short-term perceived changes but not longer term outcomes.
Can AI tools reproduce the three analytical tracks identified in PLOS One?
Answer: Yes, AI-enabled qualitative platforms can replicate and quantify analytic tracks like self-leadership, team-leadership, and workforce development when guided by human-coded seeds, according to best practices in AI-assisted qualitative research.
Using the PLOS One study as a template, researchers can seed codes from the paper, run automated clustering, and then validate results against human-coded selections to reproduce the three tracks and to measure frequency and co-occurrence.
Does the PLOS One study provide data for reuse?
Answer: The PLOS One study does not make raw interview data publicly available due to ethical restrictions, but de-identified data may be available on request under controlled access, according to PLOS One.
PLOS One explains that ethical approval (reference 2022-04052-01) restricts public sharing and that data requests require permission from the University of Gothenburg and confidentiality assessments.
Conclusion & Next Steps
The PLOS One study published on 22 July 2026 shows that a structured six-month person-centred leadership programme was experienced to change leadership practice across self, team, and workforce levels when key preconditions were present (PLOS One).
Qualitative researchers and health leaders should capture timing, managerial support, workplace assignments, and participant diversity because PLOS One links these preconditions to the depth of change.
AI-enabled qualitative analysis can accelerate coding, quantify patterns, and preserve audit trails so teams can scale insights from 13 interviews or larger cohorts efficiently.
To test these approaches on your data, Try Evidano for free.