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Evaluation of a Complex Intervention to Strengthen Participation-Centred Care for Children with Special Healthcare Needs: Protocol of the Stepped Wedge Cluster Randomised PART-CHILD Trial

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  • Michael Eichinger

    (Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
    Division of Pediatric Epidemiology, Institute of Medical Biostatistics, Epidemiology, and Informatics, University Medical Center of the Johannes Gutenberg-University Mainz, 55131 Mainz, Germany)

  • Tatiana Görig

    (Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany)

  • Sabine Georg

    (Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany)

  • Dorle Hoffmann

    (Division of Pediatric Epidemiology, Institute of Medical Biostatistics, Epidemiology, and Informatics, University Medical Center of the Johannes Gutenberg-University Mainz, 55131 Mainz, Germany)

  • Diana Sonntag

    (Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany)

  • Heike Philippi

    (Social Pediatric Centre Frankfurt, 60316 Frankfurt, Germany)

  • Jochem König

    (Division of Pediatric Epidemiology, Institute of Medical Biostatistics, Epidemiology, and Informatics, University Medical Center of the Johannes Gutenberg-University Mainz, 55131 Mainz, Germany)

  • Michael S. Urschitz

    (Division of Pediatric Epidemiology, Institute of Medical Biostatistics, Epidemiology, and Informatics, University Medical Center of the Johannes Gutenberg-University Mainz, 55131 Mainz, Germany)

  • Freia De Bock

    (Department of General Pediatrics, Neonatology and Pediatric Cardiology, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University, 40225 Düsseldorf, Germany)

Abstract

Introduction: Participation is an important dimension of healthy child development and is associated with higher self-rated health, educational attainment and civic engagement. Many children with special healthcare needs (SHCN) experience limited participation and are thus at risk for adverse health and developmental outcomes. Despite this, interventions that promote participation in healthcare are scarce. We therefore evaluate the effectiveness of a complex age- and condition-generic intervention that strengthens participation-centred care involving parents and their children with SHCN by, inter alia , assessing preferences, specifying participation goals and facilitating shared decision-making in care. Methods and analysis: In this study protocol we describe the design and procedures for an unblinded, stepped wedge, cluster randomised trial conducted in 15 German interdisciplinary healthcare facilities providing services for children aged 0–18 years with SHCN. Sites are randomised to five periods in which they switch from control to intervention condition in blocks of three. The intervention includes: (1) team training focused on participation-centred care, (2) introduction of a new software facilitating participation-focused documentation and (3) implementation support promoting the transfer of training content into routine care. Study sites deliver routine care while in the control condition. As primary outcome, the degree of perceived shared decision-making with parents (CollaboRATE pediatric parent scale), a potential antecedent of achieving participation goals in everyday life, is assessed on one randomly selected day per week during the entire study period, directly following care appointments. We aim to sample 70 parents per study site and period. Additionally, participation of children is assessed within a closed embedded cohort with three parent and patient surveys. Intervention effectiveness will be modelled with a marginal model for correlated binary outcomes using generalised estimation equations and complete cases. A comprehensive mixed-methods process evaluation complements the effectiveness analyses.

Suggested Citation

  • Michael Eichinger & Tatiana Görig & Sabine Georg & Dorle Hoffmann & Diana Sonntag & Heike Philippi & Jochem König & Michael S. Urschitz & Freia De Bock, 2022. "Evaluation of a Complex Intervention to Strengthen Participation-Centred Care for Children with Special Healthcare Needs: Protocol of the Stepped Wedge Cluster Randomised PART-CHILD Trial," IJERPH, MDPI, vol. 19(24), pages 1-18, December.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16865-:d:1004457
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    References listed on IDEAS

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    1. Lloyd A. Mancl & Timothy A. DeRouen, 2001. "A Covariance Estimator for GEE with Improved Small‐Sample Properties," Biometrics, The International Biometric Society, vol. 57(1), pages 126-134, March.
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