AI-enabled qualitative analysis is the primary method that evaluation teams can use to turn multi-session participatory workshops into actionable recommendations. According to the August 20, 2026 PLOS ONE study, researchers applied a strategy mapping software across a four-workshop series to evaluate COVID-19 crisis management, producing ten participant-developed recommendations and a follow-up interview study. This post translates the PLOS ONE case into practical guidance for qualitative researchers and evaluation teams who want to use AI to accelerate transcription, thematic coding, cross-segment analysis, and recommendation clarity.
Key Takeaways
According to the August 20, 2026 PLOS ONE case study, a strategy mapping tool run across four workshops with 15 healthcare staff produced ten recommendations and showed measurable process use in a one-year follow-up.
- 15 participants joined four workshops held between April 3, 2023 and June 12, 2023, according to the PLOS ONE study (Björnqvist et al., 2026).
- The PLOS ONE follow-up interviews were conducted in spring 2024, with ten of the original 15 participants interviewed about recommendation use, according to Björnqvist et al. (2026).
- Björnqvist et al. (2026) reported that participants rated risks and recommendations during the workshops, and the process yielded ten final recommendations validated in a June 12, 2023 session.
- Quote from the study: "Yes, I would say that it absolutely has been started. I would not, however, say that the mapping is completely done." (Interviewee 2, as quoted in Björnqvist et al., 2026).
- Quote from the study: "Sometimes you need a structured process to emphasize those things that have been known or that have been discussed." (Interviewee 4, as quoted in Björnqvist et al., 2026).
What happened and how the strategy mapping process worked
What happened: According to Björnqvist et al. (2026) in PLOS ONE, the authors ran a four-stage participatory evaluation using strategy mapping software (Strategyfinder) to elicit risks and generate recommendations with frontline healthcare staff.
How it worked: According to the PLOS ONE methods section, the process used blind gathers (anonymous item entry), open gathers (visible sharing), causal linking, centrality algorithms, and participant ratings of importance, effectiveness, and feasibility across sessions on April 3, 2023, April 6, 2023, April 24, 2023, and June 12, 2023 (Björnqvist et al., 2026).
Measurement and follow-up: According to Björnqvist et al. (2026), the output was ten participant-developed recommendations and, approximately one year later in spring 2024, ten semi-structured interviews assessed perceived use and value.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| April 3, 2023 | Workshop 1 participants | 15 employees (blind gather & causal map) | Initial risk system created; basis for subsystems (Björnqvist et al., 2026) |
| April 6, 2023 | Workshop 2 activity | Validation of 5 subsystems | Participant validation and importance ratings guided next steps (Björnqvist et al., 2026) |
| April 24, 2023 | Workshop 3 activity | Recommendation generation and effectiveness/feasibility ratings | Participants linked recommendations to central risks and rated them (Björnqvist et al., 2026) |
| June 12, 2023 | Workshop 4 activity | In-person validation and finalization | Final recommendations were validated and included in the report (Björnqvist et al., 2026) |
| Spring 2024 | Follow-up interviews | 10 of 15 participants (semi-structured) | Mixed views on use, many reported partial or in-progress implementation (Björnqvist et al., 2026) |
| August 20, 2026 | Publication | PLOS ONE article (Björnqvist et al., 2026) | Case published with dataset available on Zenodo under restricted access (Björnqvist et al., 2026) |
Implications for qualitative researchers and evaluation teams
Short answer: According to Björnqvist et al. (2026) in PLOS ONE, participatory strategy mapping plus structured ratings increases perceived actionability but can produce recommendations that need facilitator polishing to improve clarity and implementation.
Operational implications: According to the PLOS ONE authors, teams should expect to invest time in multiple sessions (the case used four workshops across April–June 2023) and plan for follow-up validation to convert participant recommendations into implementable actions (Björnqvist et al., 2026).
Design implications: According to Björnqvist et al. (2026), incorporate both individual idea elicitation (blind gather) and collective mapping to surface diverse perspectives, then use participant ratings and centrality metrics to prioritize targets for recommendation development.
Measurement implications: According to Björnqvist et al. (2026), define "use" up front and collect structured implementation logs or KPIs; the study relied on spring 2024 interviews and noted variability in how participants construed "use."
How Evidano helps: map problems to AI-enabled solutions
Problem: multi-session transcripts and slow synthesis → Solution: fast transcription and thematic coding
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Practically, when teams run multi-session workshops like those in the PLOS ONE case (April–June 2023), manual transcription and iterative coding slowed synthesis; Björnqvist et al. (2026) transcribed interviews with Microsoft Word speech-to-text and manual correction, which consumed time.
Evidano feature: automated transcription with custom dictionaries and PII redaction reduces cleanup time compared to manual correction, enabling analysts to move to coding and mapping faster. See Evidano features.
Problem: turning participant notes into prioritized recommendations → Solution: thematic, frequency, and cross-segment analysis
According to Björnqvist et al. (2026), participants generated many risks and recommendations that required clustering, causal linking, and centrality-based prioritization.
Evidano feature: automated thematic extraction plus frequency and cross-segment comparisons highlight which recommendations are most discussed by role or session; this reproduces the participant ratings and centrality analyses reported in the PLOS ONE study while adding reproducible counts and filters.
Problem: ambiguous recommendations need polishing → Solution: AI-assisted recommendation refinement and facilitator workflows
According to the PLOS ONE discussion, recommendations sometimes felt "too vague" or dependent on other units, and the authors suggested increased facilitator involvement to improve clarity (Björnqvist et al., 2026).
Evidano feature: AI-assisted drafting tools can propose clearer, more actionable recommendation phrasing and produce versioned reports that facilitators can review with participants, preserving ownership while raising actionability.
Problem: follow-up impact assessment is inconsistent → Solution: integrated tracking and AI chat for stakeholders
According to Björnqvist et al. (2026), impact measurement relied on interviews a year later with variable understandings of what "use" meant.
Evidano feature: timeline tracking, exportable recommendation lists, and an AI chat over project documents make it easier to ask stakeholders directed follow-up questions and generate consistent implementation metrics for future evaluations.
FAQ: ai-enabled qualitative analysis
How does strategy mapping differ from other participatory methods?
Answer: Strategy mapping emphasizes causal linking, visual maps, and participant ratings to create actionable recommendations.
Supporting detail: According to Björnqvist et al. (2026), strategy mapping builds on causal mapping and SODA methods and adds clustering, centrality metrics, and interactive ratings to focus on implementable actions rather than only impact measurement.
Can AI be used in the participatory mapping process without replacing participants?
Answer: Yes, according to Björnqvist et al. (2026) AI can assist translation and visualization while participant ownership of content remains central.
Supporting detail: In the PLOS ONE study the authors used a large language model for translation and map recreation only, and explicitly stated the model "was not used for data generation, analysis, or interpretation" (Björnqvist et al., 2026).
What practical metrics should evaluators collect after a participatory evaluation?
Answer: Define and collect baseline implementation markers, e.g., start date, planning status, partial implementation, full implementation with dates, and responsible owner.
Supporting detail: Björnqvist et al. (2026) recommend defining what "use" means because their spring 2024 interviews revealed varied participant interpretations of recommendation use.
How many sessions are optimal for a participatory strategy mapping evaluation?
Answer: There is no single optimal number, but Björnqvist et al. (2026) used four workshops and suggest shorter alternatives may work with facilitator adjustments.
Supporting detail: The PLOS ONE authors noted trade-offs: four sessions supported reflection and depth but created scheduling burdens and some participant drop-off, and they proposed condensed two-workshop variants warrant further research (Björnqvist et al., 2026).
Conclusion & Next Steps
Bottom line: According to Björnqvist et al. (2026) in PLOS ONE, strategy mapping software run in participatory workshops can produce actionable recommendations, but teams must plan for facilitator refinement and structured follow-up to maximize impact.
Next steps for teams: use AI to speed transcription and thematic coding, reproduce participant ratings and centrality metrics, and run shorter validation loops so recommendations become implementable in months rather than years (Björnqvist et al., 2026).
If you want to test an AI-first workflow that supports transcription, thematic and frequency analysis, and facilitator-friendly reporting, explore how Evidano applies these features to evaluation work in Evidano features.
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Topics
- ai-enabled qualitative analysis
- participatory evaluation
- strategy mapping software
- qualitative synthesis
- evaluation workshops
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