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Scale SDM: Shared decision-making in pediatric PT

Evidano6 min read

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

ItemValueSource / note
PublicationJune 30, 2026 (PLOS ONE)Limmen et al., PLOS ONE
Study phasesPhase 1: focus groups + survey; Phase 2: model adaptationMethods section
Focus groupsAdolescents n=11; Parents n=9; PPTs n=6 (total n=26)Conducted Jun 30, 2023 to Mar 5, 2024
Survey46 pediatric physical therapists (response rate 90%)Validation of focus-group themes
Core recommendationsStart SDM at intake/goal-setting; repeat throughout therapy; adapt involvement to child age and family contextResults & Fig 2
Top barriersTime constraints; balancing multiple perspectives; variable therapist skillsCFIR mapping
Implementation strategiesTraining, SDM tools, protected learning time, team champions, parent/child empowermentCFIR-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.

What this means for qualitative researchers & clinical teams: shared decision-making in pediatric physical therapy

For UX/health researchers

Qualitative researchers should note the dataset is small (focus groups n=26; survey n=46) but deep, suitable for thematic saturation and framework mapping. Reproducible coding from open codes to themes to CFIR domains is feasible and recommended, and researchers should use cross-segment analysis to compare adolescent versus parent versus therapist language around responsibility and time.

For implementation leads / clinic managers

Clinic managers should build protected time for SDM training, appoint local champions, and introduce simple SDM prompts at intake and evaluations. Measure change with PROMs and periodic thematic checks of clinician notes to detect shifts in topic frequency such as 'home program' and 'goal ownership'.

For policy & quality teams

Policy and quality teams should standardize documentation touchpoints (intake goal talk, choice talk, option talk, decision talk, evaluation) in EMRs or templates to create audit trails for implementation research. The adapted model in Figure 2 requires consistent documentation to support audits and improvement cycles.

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.

FAQ: shared decision-making in pediatric physical therapy

Can AI accurately map themes to CFIR?

Yes, AI can map themes to CFIR when you provide a validated codebook and perform human review. Evidano supports codebook import and human-in-the-loop validation to ensure conceptual fidelity.

Will automating coding lose nuance about child versus parent perspectives?

No, automating coding does not have to lose nuance if teams run segment-level tagging and cross-segment frequency and co-occurrence analyses and apply human confirmation. Evidano speeds identification while preserving human interpretation through review workflows.

How do we measure if SDM uptake improves?

Measure SDM uptake using a combination of PROMs, scheduled audits of documented SDM touchpoints, and thematic trend analysis, such as an increase in 'goal ownership' language among adolescents. Use these measures with periodic qualitative checks to validate change.

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

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