Codesigning patient experience measures for and with children and young people with intellectual disability: a study protocol.

INTRODUCTION: Children and young people with intellectual disability represent one of the most vulnerable groups in healthcare, yet they remain under-represented in projects to design, develop and/or improve healthcare service delivery. Increasingly, healthcare services are using various codesign and coproduction methodologies to engage children and young people in service delivery improvements. METHODS AND ANALYSIS: This study employs an inclusive approach to the study design and execution, including two co-researchers who are young people with intellectual disability on the project team. We will follow an adapted experience-based co-design methodology to enable children and young people with intellectual disability to participate fully in the co-design of a prototype tool for eliciting patient experience data from children and young people with intellectual disability in hospital. ETHICS AND DISSEMINATION: This study was granted ethical approval on 1 February 2021 by the Sydney Children's Hospitals Network Human Research Ethics Committee, reference number 2020/ETH02898. Dissemination plan includes publications, doctoral thesis chapter, educational videos. A summary of findings will be shared with all participants and presented at the organisation quality and safety committee.

Improving Metadata Infrastructure for Complex Surveys: Insights from the Fragile Families Challenge.

Researchers rely on metadata systems to prepare data for analysis. As the complexity of data sets increases and the breadth of data analysis practices grow, existing metadata systems can limit the efficiency and quality of data preparation. This article describes the redesign of a metadata system supporting the Fragile Families and Child Wellbeing Study on the basis of the experiences of participants in the Fragile Families Challenge. The authors demonstrate how treating metadata as data (i.e., releasing comprehensive information about variables in a format amenable to both automated and manual processing) can make the task of data preparation less arduous and less error prone for all types of data analysis. The authors hope that their work will facilitate new applications of machine-learning methods to longitudinal surveys and inspire research on data preparation in the social sciences. The authors have open-sourced the tools they created so that others can use and improve them.