Evidano is an AI-powered qualitative data analysis platform that auto-transcribes interviews, redacts PII, extracts themes, and helps map codes to implementation frameworks like CFIR. Shared decision-making (SDM) in pediatric physical therapy is practical but under-implemented. This post refracts a June 30, 2026 PLoS ONE qualitative study into a repeatable qualitative analysis workflow you can run with Evidano. You’ll get: a succinct summary of findings (who, when, what), the main barriers and facilitators identified by adolescents, parents and therapists, and a 7-step Evidano playbook to extract themes, cross-segment differences, and implementation strategies from transcripts or survey responses. Use this to speed synthesis, produce stakeholder-ready visuals, and map quotes to CFIR domains for implementation planning. Ethics note: these insights are for research and quality improvement, not clinical diagnosis; always preserve consent and confidentiality.
Key Takeaways
The June 30, 2026 PLoS ONE qualitative study shows that SDM in pediatric physical therapy should be goal-based, starting at intake and continuing through periodic evaluations, with time constraints and multiple perspectives as primary barriers.
The study maps themes to CFIR and recommends multifaceted implementation strategies; this post presents a 7-step Evidano workflow to extract themes, compare segments, and generate implementation plays.
- SDM timeline: apply SDM at intake (goal-setting), during option discussions, decision-making, and periodic evaluation.
- Top barriers: time constraints and balancing multiple perspectives were reported across CFIR domains.
- Top facilitators: adaptability of SDM conversations and a supportive practice culture enable tailored implementation.
- Implementation levers: training, practical SDM tools, protected learning time, champions, and team reflection.
Study snapshot: what the table shows
This table summarizes key metrics and outputs from the June 30, 2026 PLoS ONE study on goal-based SDM in pediatric physical therapy.
The table below aggregates publication date, focus group details, survey validation, primary outputs, and top barriers and facilitators as reported by the study.
Findings snapshot
| Metric | Value | Source / Note |
|---|---|---|
| Publication date | June 30, 2026 | PLoS ONE |
| Focus groups | 6 groups (June 30, 2023–Mar 5, 2024) | Adolescents n=11; Parents n=9; PPTs n=6 |
| Validation survey | 46 pediatric PTs (response rate 90%) | Qualitative open responses |
| Primary outputs | Adapted goal-based SDM model + implementation strategies | Mapped to CFIR & CFIR-ERIC |
| Top barriers | Time constraints; balancing multiple perspectives | Reported across CFIR domains |
| Top facilitators | Adaptability of SDM; supportive practice culture | Enables tailored conversations |
Fast take: why this matters (source)
The PLoS ONE paper published June 30, 2026 adapted a goal-based SDM model for pediatric physical therapy and identified barriers and facilitators through qualitative methods.
Read the original paper at PLoS ONE.
- Key payoff: SDM should start at intake/goal-setting and continue through evaluations; barriers include time and multiple perspectives; facilitators include adaptable conversations and supportive practice culture.
- This study maps interview codes onto CFIR domains and recommends multifaceted implementation strategies (training, tools, team culture).
What happened: SDM in pediatric physical therapy (qualitative analysis)
The authors conducted six focus groups and a qualitative survey to identify how SDM should be applied in pediatric physical therapy.
The authors ran six focus groups (adolescents, parents, pediatric physical therapists) and a qualitative survey of 46 PPTs to validate findings, coded transcripts inductively for themes, and mapped themes deductively to CFIR domains to identify barriers and facilitators.
- Where SDM applies: intake (preparation), explicit goal talk, choice/options talk, decision talk, and periodic evaluation.
- What to discuss: frequency, duration, homework/home context, expectations of family and therapist, and referrals.
- Implementation levers: training, practical SDM tools, protected learning time, champions and team reflection.
Implications for researchers and clinicians
For qualitative researchers / UX teams
Qualitative researchers and UX teams should map transcripts to implementation frameworks like CFIR to surface actionable barriers.
The study demonstrates coding to CFIR linking as a repeatable pipeline and recommends comparing segments (adolescents vs parents vs therapists) to locate alignment gaps and target interventions (for example, parent empowerment vs therapist communication).
For clinic managers & implementation leads
Clinic managers and implementation leads should invest in short training and protected practice time because clinicians report early time costs that decline with experience.
The study recommends using champions and team reflection sessions to normalize SDM and monitor fidelity through scheduled evaluation moments.
For pediatric physical therapists
Pediatric physical therapists should start SDM at intake with shared goal setting before presenting options because goals guide option feasibility and adherence.
Therapists should ask families and adolescents explicitly how they want to be involved, adapt pacing, and use visual aids for lower health literacy.
Do more, faster with Evidano
Problem: Hundreds of interview minutes; slow coding
Evidano auto-transcribes interviews and redacts PII to reduce manual transcription and review time.
Solution: Auto-transcribe interviews (custom dictionary for clinical terms) and redact PII automatically in Evidano, then run an initial thematic extraction to surface candidate codes and high-frequency phrases.
Problem: Mapping themes to implementation frameworks
Evidano helps map themes to frameworks by allowing import of a codebook or suggesting codes and applying mappings at scale.
Solution: Import codebook or let Evidano suggest codes, then tag excerpts to CFIR domains. Generate a barrier/facilitator frequency matrix and export tables for CFIR-ERIC matching.
Problem: Comparing perspectives (adolescents vs parents vs therapists)
Evidano produces cross-segment analyses that show side-by-side theme frequencies and representative quotes to reveal alignment gaps quickly.
Solution: Use Evidano cross-segment analysis to produce side-by-side theme frequencies, salient quotes, and co-occurrence networks that reveal alignment gaps quickly.
Problem: Communicating findings to stakeholders
Evidano generates visual exports and AI summaries to create stakeholder-ready briefs with representative quotes and suggested implementation plays.
Solution: Create visual exports (word clouds, hierarchical code trees, co-occurrence graphs) and an AI-enabled research brief that summarizes methods, top themes, representative quotes, and suggested implementation plays. Data is encrypted and never used to train third-party models.
7-step checklist: from transcripts to implementation playbook (Evidano)
This 7-step checklist is a short, repeatable workflow to reproduce the study’s outputs in your context using Evidano.
- 1) Import recordings, meeting notes, and survey responses into Evidano; enable PII redaction and set a custom dictionary for clinical terms.
- 2) Auto-transcribe and review transcripts; attach demographics (age, role, practice) as segments.
- 3) Run automatic thematic extraction; review and refine code hierarchy (goal talk, choice talk, evaluation, barriers, facilitators).
- 4) Map codes to CFIR domains using a custom mapping table; generate a barrier/facilitator frequency matrix.
- 5) Produce cross-segment comparisons (adolescent vs parent vs therapist) and extract representative quotes per theme.
- 6) Create visuals (co-occurrence network, hierarchical code → subcodes) and export an implementation playbook linked to CFIR-ERIC strategies.
- 7) Iterate: run targeted AI avatar interviews or follow-up surveys to probe high-priority barriers and measure change.
FAQ: SDM in pediatric physical therapy
What is the quickest way to compare perspectives across groups?
The quickest way to compare perspectives across groups is to tag each transcript with participant role and run cross-segment theme frequency and quote extraction.
Tag each transcript with the participant role and run Evidano cross-segment theme frequency and quote extraction to find divergences in minutes.
How do I preserve confidentiality while using AI?
You preserve confidentiality by using PII redaction, pseudonymization, and encrypted storage provided by the analysis platform.
Use PII redaction, pseudonymization, and Evidano’s encrypted data storage. Evidano does not use customer data to train third-party models.
Can Evidano map codes to CFIR or other frameworks?
Evidano can map codes to CFIR or other frameworks by importing or creating a code→framework mapping and bulk-applying it across excerpts.
Import or create a code→framework mapping and bulk-apply it across coded excerpts to generate barrier/facilitator reports tied to implementation strategies.
Wrapping up & next steps
The PLoS ONE study (June 30, 2026) provides an adapted goal-based SDM model and a prioritized list of implementation strategies for pediatric physical therapy.
If you run qualitative analyses of SDM in pediatric physical therapy or any multi-stakeholder clinical setting, use an AI-enabled pipeline to compress coding time, compare segments reliably, and produce stakeholder-ready implementation plays.
- Try the 7-step workflow above in your next pilot and link themes to CFIR to choose tailored ERIC strategies.
- Ready to scale? Try Evidano for free to import transcripts, map to CFIR, and generate an implementation playbook with visuals and extractable quotes.
Ethics reminder: use these methods for research, quality improvement and implementation planning only; maintain informed consent and data protection.
Sources
Topics
- SDM pediatric physical therapy
- shared decision making
- qualitative analysis
- CFIR
- implementation strategies
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