The primary question: what should a physiotherapist navigator role do in acute cancer care, and how can qualitative methods make that role trial-ready? According to the PLOS One study published August 7, 2026, a two-round modified Delphi of Canadian practitioners generated a consensus PT Navigator role description and specific role domains. This post is written for qualitative researchers, clinical trial teams, and health services designers who want to apply AI-enabled qualitative research to design, code, and prepare role-based interventions for pilot trials.
Key Takeaways
A PLOS One Delphi study (Edward et al., published August 7, 2026) used two online survey rounds and a consensus meeting to define a physiotherapist navigator role and reached consensus on all role items.
AI-enabled qualitative research can speed the translation of open-text Delphi feedback into a finalized role description by automating content analysis, mapping themes to role domains, and producing extractable metrics for trial design.
- According to the PLOS One study (published August 7, 2026), Round One had n = 34 participants and Round Two had n = 18 participants.
- According to the PLOS One study (published August 7, 2026), perceived usefulness of the proposed role scored mean 9.19/10 (SD 2.12) in Round One.
- According to the PLOS One study (published August 7, 2026), the research incorporated two survey rounds (Jan 8 to Mar 19, 2025 and Apr 2 to Apr 16, 2025) and a consensus meeting in June 2025.
What happened and how the Delphi was run
A two-round modified Delphi engaged Canadian clinicians to iteratively refine a PT Navigator role description and reach consensus on domain items.
According to the PLOS One study (Edward et al., published August 7, 2026), Round One ran from Jan 8 to Mar 19, 2025 with 34 respondents and collected open-ended qualitative feedback; Round Two ran Apr 2 to Apr 16, 2025 with 18 respondents and collected quantitative agreement ratings.
The study predefined inclusion rules: items suggested by ≥10% of respondents were added to Round Two, and consensus in Round Two was defined as a mean score of 5.25/7 (75%), according to Edward et al. in PLOS One (2026).
The study concluded with an online consensus meeting in June 2025 that included seven end users and approved the final role description for piloting.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| Jan 8–Mar 19, 2025 | Round One respondents | n = 34 | Collected qualitative suggestions that generated seven new items for Round Two (most requested: more objective testing, n = 10/34). |
| Apr 2–Apr 16, 2025 | Round Two respondents | n = 18 | All items met the predefined consensus threshold (≥75% agreement) and required no further modifications. |
| Round One (reported in paper) | Perceived usefulness score | Mean 9.19/10, SD 2.12 | Strong clinician support for piloting the PT Navigator role. |
| Study reporting (Aug 7, 2026) | Consensus rule | Consensus defined as mean ≥5.25/7 (75%) | Provides a reproducible threshold to decide which role items to include in a pilot RCT. |
Implications for qualitative researchers and trial teams
Immediate answer: the Delphi produced a concrete role description and priority domains that trial teams can operationalize.
According to the PLOS One study (Edward et al., published August 7, 2026), the four domains that define the PT Navigator role are process, assessment, triaging decisions, and duties and responsibilities, which gives qualitative coders a ready taxonomy for thematic coding.
Qualitative researchers should note that the PLOS One team added items proposed by ≥10% of respondents and used content analysis in duplicate to process open-text feedback, a workflow that can be combined with AI-assisted coding to accelerate synthesis while preserving coder reconciliation.
Trial teams should capture both patient-reported and objective outcome measures, because Edward et al. (PLOS One, 2026) report strong support for adding objective testing and highlight that patient-rated measures alone may miss functional loss.
How Evidano helps (problem → feature mappings)
Problem: Open-text Delphi feedback is slow to synthesize
Solution: Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano automates duplicate content coding, extracts theme frequencies, and produces exportable codebooks so teams can convert Round One open-text suggestions (like the n = 34 responses in the PLOS One study) into a structured role description faster.
Problem: Mapping free-text suggestions to role domains introduces bias
Solution: Evidano supports duplicate coding, coder reconciliation workflows, and AI-assisted thematic extraction to reduce single-coder bias and to match the PLOS One study’s duplicate content analysis approach.
Evidano’s cross-segment analysis lets trialists compare suggestions by profession or province, replicating how the PLOS One authors examined diverse clinician perspectives before the June 2025 consensus meeting.
Problem: Teams need quantitative metrics from qualitative data for trial design
Solution: Evidano generates frequency counts, co-occurrence matrices, and visualizations that translate qualitative items into quantitative inputs for sample-size and outcome planning.
For example, Evidano can turn the PLOS One Round One finding that 29.4% (10/34) requested more objective testing into a dashboard metric that informs which assessments to include in a pilot RCT.
Problem: Preparing role materials and figures for a consensus meeting
Solution: Evidano produces clean exportable summaries, code-to-text excerpts, and visualization assets that teams can use directly in consensus meeting slide decks (see Evidano features).
Evidano’s secure data handling and document import supports mixed inputs: surveys, transcripts, and literature sources, which helps teams reproduce the PLOS One study’s mixed-methods workflow safely.
Conclusion & Next Steps
The PLOS One Delphi study (Edward et al., published August 7, 2026) produced a consensus physiotherapist navigator role that trial teams can operationalize across four clear domains.
AI-enabled qualitative research shortens the path from open-text Delphi feedback to a trial-ready role description by automating coding, quantifying theme frequencies, and producing exportable summaries suitable for consensus meetings.
If you are designing a pilot RCT of a navigator role, combine the PLOS One role taxonomy with AI-assisted thematic synthesis to speed protocol development and measurement selection.
To experiment with AI-assisted qualitative pipelines, Try Evidano for free.
Topics
- physiotherapist navigator role
- PT navigator role
- Delphi study physiotherapy
- AI qualitative analysis
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