Evidano is an AI-powered qualitative data analysis platform that ingests transcripts, applies PII redaction and language normalization, supports AI-assisted coding, and maps themes to frameworks like CFIR. The PLoS study by Limmen et al. (published June 30, 2026) identifies when SDM should start (intake/goal-setting), who to involve (children, parents, therapists), and key operational barriers. Read the original study in PLOS ONE: PLOS ONE.
Key Takeaways
Limmen et al. (PLOS ONE, June 30, 2026) show that shared decision-making (SDM) in pediatric physical therapy should begin at intake and goal-setting, recur across therapy, and be adapted to child age and family context. Convert the study's qualitative insights into reproducible implementation steps by instrumenting intake/evaluation touchpoints, mapping themes to CFIR, and measuring change with PROMs and thematic monitoring.
points: ["Limmen et al. used exploratory focus groups (adolescents n=11; parents n=9; pediatric physical therapists n=6; total n=26) and a qualitative survey (n=46) to adapt a goal-based SDM model (PLOS ONE, Jun 30, 2026).", "Start SDM at intake/goal-setting and repeat SDM conversations during informal session checks and formal evaluations, adapting involvement to child age and family context.", "Top barriers are time constraints, balancing multiple perspectives, and variable therapist skills; implementation strategies include training, SDM tools, protected learning time, and local champions."]
Fast take + source
Fast take: Limmen et al. (PLOS ONE, June 30, 2026) explored shared decision-making in pediatric physical therapy using six focus groups (adolescents n=11, parents n=9, pediatric physical therapists n=6) and a qualitative survey of 46 pediatric physical therapists, then adapted a goal-based SDM model and recommended multifaceted implementation strategies. Full paper: PLOS ONE
Findings snapshot
| Item | Value | Source / note |
|---|---|---|
| Publication | June 30, 2026 (PLOS ONE) | Limmen et al., PLOS ONE |
| Study phases | Phase 1: focus groups + survey; Phase 2: model adaptation | Methods section |
| Focus groups | Adolescents n=11; Parents n=9; PPTs n=6 (total n=26) | Conducted Jun 30, 2023 to Mar 5, 2024 |
| Survey | 46 pediatric physical therapists (response rate 90%) | Validation of focus-group themes |
| Core recommendations | Start SDM at intake/goal-setting; repeat throughout therapy; adapt involvement to child age and family context | Results & Fig 2 |
| Top barriers | Time constraints; balancing multiple perspectives; variable therapist skills | CFIR mapping |
| Implementation strategies | Training, SDM tools, protected learning time, team champions, parent/child empowerment | CFIR-ERIC + Behavior Change Wheel |
How the study gathered evidence and what it means
The study gathered evidence in two qualitative phases: exploratory focus groups and a validating qualitative survey. The authors ran focus groups with adolescents, parents, and pediatric physical therapists, then used a qualitative survey (n=46) to validate themes, applying inductive coding for 'how/when' SDM applies and deductive CFIR mapping to identify implementation barriers and facilitators.
- SDM best-practice window: begins at intake and goal setting, recurs during informal session-level checks and formal evaluations.
- Decisions suited for SDM: treatment frequency, duration, homework, home feasibility and referral options.
- Context matters: adapt SDM conversations to child age, developmental stage, health literacy and family resources.
Do more, faster with Evidano (mapped to the study's needs)
Problem: scattered qualitative data + manual coding
Evidano ingests transcripts, meeting notes, and survey responses so teams can run automated thematic analysis calibrated with their codebook and export CFIR-domain tagging. Use Evidano to accelerate open-coding, reduce manual effort, and produce reproducible outputs for implementation planning.
Problem: multilingual / messy transcripts and PII
Evidano applies transcription with custom dictionary, PII redaction, and translation so teams can compare quotes across Dutch and English sources without leaking identifiers. These features support cross-language synthesis while protecting sensitive pediatric data.
Problem: inconsistent coding across analysts
Evidano supports codebook import and AI-assisted pre-tagging so analysts can review suggestions and produce reproducible inter-rater-ready outputs. Human-in-the-loop validation preserves nuance while increasing throughput.
Problem: linking findings to implementation frameworks
Evidano automatically maps themes to frameworks like CFIR and generates co-occurrence networks (for example, 'time constraints' plus 'revenue model') and stakeholder-ready visualizations. These outputs help translate qualitative themes into implementation strategy checklists.
Security & compliance
Evidano uses encrypted storage and proprietary LLMs tuned for qualitative research, and Evidano does not use customer data to train third-party models, a critical consideration for sensitive pediatric transcripts.
Checklist: 7-step pilot to reproduce Limmen et al.'s analysis in Evidano
This checklist gives a minimal reproducible workflow you can run in 2 to 4 weeks to reproduce Limmen et al.'s approach and map themes to CFIR.
- 1) Gather inputs: focus group audio/transcripts, survey responses, clinician notes, and PROMs.
- 2) Ingest to Evidano and apply PII redaction and language normalization.
- 3) Run automated open-coding and review suggested code clusters, iteratively refining the codebook.
- 4) Map final themes to CFIR domains using Evidano’s framework-mapping feature.
- 5) Produce cross-segment frequency tables (adolescent vs parent vs pediatric physical therapist) and co-occurrence networks.
- 6) Export visualizations and an evidence-backed implementation checklist (training topics; timing for SDM touchpoints).
- 7) Share an interactive report with clinicians and collect feedback for the next iteration.
Ethics note
This is research-focused guidance, not clinical advice. Limmen et al. withheld full transcripts for privacy, and any reuse of sensitive qualitative data requires IRB and consent alignment plus careful PII handling.
Wrapping up & next steps
The study gives a practical, goal-based SDM model and a shortlist of implementation strategies anchored in CFIR that teams can operationalize. Turn Limmen et al.'s qualitative insights into operational programs by instrumenting intake and evaluation touchpoints, measuring change with PROMs and thematic monitoring, and automating repetitive synthesis tasks with Evidano.
- Start small: pilot the 7-step workflow above on one clinic's transcripts.
- If you want a demo that maps transcripts to CFIR and produces an implementation-ready report from your own files, Try Evidano for free.
Topics
- shared decision-making pediatric physical therapy
- CFIR mapping
- AI qualitative analysis
- Evidano
- SDM implementation
Keep reading
- Commentary on NewsFaster Insights: AI-enabled qualitative researchAI-enabled qualitative research accelerates analysis of implementation studies like the SAVING Program; learn key stats, quotes, and how Evidano speeds mixed-methods synthesis.
- Commentary on NewsPrimary Care Nutrition Training: Qualitative InsightsEvidence-based steps to improve primary care nutrition training using qualitative insights and AI-enabled analysis; read practical implications and next steps for educators.
- Commentary on NewsRelational Barriers: help-seeking social networksPLOS One (30 Jul 2026) maps help-seeking social networks for people with criminal justice contact; learn how AI qualitative analysis reveals relational barriers and intervention targets.
