Psychometric Properties of the Young Children's Participation and Environment Measure

AbstractObjectiveTo evaluate the psychometric properties of the newly developed Young Children's Participation and Environment Measure (YC-PEM).DesignCross-sectional study.SettingData were collected online and by telephone.ParticipantsConvenience and snowball sampling methods were used to survey caregivers of children (N=395, comprising children with [n=93] and without [n=302] developmental disabilities and delays) between the ages of 0 and 5 years (mean age ± SD, 35.33±20.29mo) and residing in North America.InterventionsNot applicable.Main Outcome MeasuresThe YC-PEM includes 3 participation scales and 1 environment scale. Each scale is assessed across 3 settings: home, daycare/preschool, and community. Data were analyzed to derive estimates of internal consistency, test-retest reliability, and construct validity.ResultsInternal consistency ranged from .68 to .96 and .92 to .96 for the participation and environment scales, respectively. Test-retest reliability (2–4wk) ranged from .31 to .93 for participation scales and from .91 to .94 for the environment scale. One of 3 participation scales and the environment scale demonstrated significant group differences by disability status across all 3 settings, and all 4 scales discriminated between disability groups for the daycare/preschool setting. The participation scales exhibited small to moderate positive associations with functional performance scores.ConclusionsResults lend initial support for the use of the YC-PEM in research to assess the participation of young children with disabilities and delays in terms of (1) home, daycare/preschool, and community participation patterns; (2) perceived environmental supports and barriers to participation; and (3) activity-specific parent strategies to promote participation

Early childhood development strategy for the world's children with disabilities

Early childhood is foundational for optimal and inclusive lifelong learning, health and well-being. Young children with disabilities face substantial risks of sub-optimal early childhood development (ECD), requiring targeted support to ensure equitable access to lifelong learning opportunities, especially in low- and middle-income countries. Although the Sustainable Development Goals, 2015–2030 (SDGs) emphasise inclusive education for children under 5 years with disabilities, there is no global strategy for achieving this goal since the launch of the SDGs. This paper explores a global ECD framework for children with disabilities based on a review of national ECD programmes from different world regions and relevant global ECD reports published since 2015. Available evidence suggests that any ECD strategy for young children with disabilities should consists of a twin-track approach, strong legislative support, guidelines for early intervention, family involvement, designated coordinating agencies, performance indicators, workforce recruitment and training, as well as explicit funding mechanisms and monitoring systems. This approach reinforces parental rights and liberty to choose appropriate support pathway for their children. We conclude that without a global disability-focussed ECD strategy that incorporates these key features under a dedicated global leadership, the SDGs vision and commitment for the world’s children with disabilities are unlikely to be realised

Medical home primary care components and current educational service use in children and youth on the autism spectrum

IntroductionChildren and youth on the autism spectrum and their families use health and educational services to address their complex needs. They use primary health care services in the medical home, as endorsed by the American Academy of Pediatrics (AAP). They can also use educational services for their cognitive, social, and adaptive skill development, beginning in early intervention and through their transition to postsecondary or vocational roles. Medical and educational services are organized and delivered in separate systems, thereby placing the primary responsibility for coordinating these services on their families.MethodsPooled data from 2016 through 2019 National Survey of Children's Health were used to measure the association between current educational service use and six medical home primary care components, controlling for select sociodemographic and clinical factors in children and youth on the autism spectrum (n = 1,922).ResultsAfter controlling for select sociodemographic and clinical factors, difficulty getting referrals [aOR = 2.93, 95% CI (1.33, 6.41), P = 0.007] and no shared decision-making in the medical home [aOR = 2.93, 95% CI (1.21, 7.06), P = 0.016] resulted in higher likelihood of current educational service use. Older children had a lower likelihood of current educational service use [aOR = 0.91, 95% CI (0.85, 0.97), P = 0.003], whereas higher autism severity increased the likelihood of current educational service use [aOR = 1.80, 95% CI (1.10, 2.95), P = 0.019].ConclusionChildren and youth on the autism spectrum, especially those with moderate or severe autism, had a higher likelihood of education service use, unless they were older, had difficulty getting referrals, and no shared decision-making. Results suggest that the way services are currently provided between health and educational systems separates medical and educational professionals, therefore increasing the demands on caregivers and educational systems to facilitate current educational service use. Further study is needed for improving the medical home referral or shared decision-making pathways and to identify caregiver strategies for navigating educational systems

CareCorpus: a corpus of real-world solution-focused caregiver strategies for personalized pediatric rehabilitation service design

In pediatric rehabilitation services, one intervention approach involves using solution-focused caregiver strategies to support children in their daily life activities. The manual sharing of these strategies is not scalable, warranting need for an automated approach to recognize and select relevant strategies. We introduce CareCorpus, a dataset of 780 real-world strategies written by caregivers. Strategies underwent dual-annotation by three trained annotators according to four established rehabilitation classes (i.e., environment/context, n=325 strategies; a child’s sense of self, n=151 strategies; a child’s preferences, n=104 strategies; and a child’s activity competences, n=62 strategies) and a no-strategy class (n=138 instances) for irrelevant or indeterminate instances. The average percent agreement was 80.18%, with a Cohen’s Kappa of 0.75 across all classes. To validate this dataset, we propose multi-grained classification tasks for detecting and categorizing strategies, and establish new performance benchmarks ranging from F1=0.53-0.79. Our results provide a first step towards a smart option to sort caregiver strategies for use in designing pediatric rehabilitation care plans. This novel, interdisciplinary resource and application is also anticipated to generalize to other pediatric rehabilitation service contexts that target children with developmental need.

CareCorpus: a corpus of real-world solution-focused caregiver strategies for personalized pediatric rehabilitation service design

In pediatric rehabilitation services, one intervention approach involves using solution-focused caregiver strategies to support children in their daily life activities. The manual sharing of these strategies is not scalable, warranting need for an automated approach to recognize and select relevant strategies. We introduce CareCorpus, a dataset of 780 real-world strategies written by caregivers. Strategies underwent dual-annotation by three trained annotators according to four established rehabilitation classes (i.e., environment/context, n=325 strategies; a child’s sense of self, n=151 strategies; a child’s preferences, n=104 strategies; and a child’s activity competences, n=62 strategies) and a no-strategy class (n=138 instances) for irrelevant or indeterminate instances. The average percent agreement was 80.18%, with a Cohen’s Kappa of 0.75 across all classes. To validate this dataset, we propose multi-grained classification tasks for detecting and categorizing strategies, and establish new performance benchmarks ranging from F1=0.53-0.79. Our results provide a first step towards a smart option to sort caregiver strategies for use in designing pediatric rehabilitation care plans. This novel, interdisciplinary resource and application is also anticipated to generalize to other pediatric rehabilitation service contexts that target children with developmental need.

Cultural adaptation of a pediatric functional assessment for rehabilitation outcomes research

Abstract Background Significant racial and ethnic health care disparities experienced by Hispanic children with special health care needs (CSHCN) create barriers to enacting culturally competent rehabilitation services. One way to minimize the impact of disparities in rehabilitation is to equip practitioners with culturally relevant functional assessments to accurately determine service needs. Current approaches to culturally adapting assessments have three major limitations: use of inconsistent translation processes; current processes assess for some, but not all, elements of cultural equivalence; and limited evidence to guide decision making about whether to undertake cultural adaptation with and without language translation. The aims of this observational study are (a) to examine similarities and differences of culturally adapting a pediatric functional assessment with and without language translation, and (b) to examine the feasibility of cultural adaptation processes. Methods The Young Children’s Participation and Environment Measure (YC-PEM), a pediatric functional assessment, underwent cultural adaptation (i.e., language translation and cognitive testing) to establish Spanish and English pilot versions for use by caregivers of young CSHCN of Mexican descent. Following language translation to develop a Spanish YC-PEM pilot version, 7 caregivers (4 Spanish-speaking; 3 English-speaking) completed cognitive testing to inform decisions regarding content revisions to English and Spanish YC-PEM versions. Participant responses were content coded to established cultural equivalencies. Coded data were summed to draw comparisons on the number of revisions needed to achieve cultural equivalence between the two versions. Feasibility was assessed according to process data and data quality. Results Results suggest more revisions are required to achieve cultural equivalence for the translated (Spanish) version of the YC-PEM. However, issues around how the participation outcome is conceptualized were identified in both versions. Feasibility results indicate that language translation processes require high resource investment, but may increase translation quality. However, use of questionnaires versus interview methods for cognitive testing may have limited data saturation. Conclusions Results lend preliminary support to the need for and feasibility of cultural adaptation with and without language translation. Results inform decisions surrounding cultural adaptations with and without language translation and thereby enhance cultural competence and quality assessment of healthcare need within pediatric rehabilitation

Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review

BackgroundIn the last decade, there has been a rapid increase in research on the use of artificial intelligence (AI) to improve child and youth participation in daily life activities, which is a key rehabilitation outcome. However, existing reviews place variable focus on participation, are narrow in scope, and are restricted to select diagnoses, hindering interpretability regarding the existing scope of AI applications that target the participation of children and youth in a pediatric rehabilitation setting. ObjectiveThe aim of this scoping review is to examine how AI is integrated into pediatric rehabilitation interventions targeting the participation of children and youth with disabilities or other diagnosed health conditions in valued activities. MethodsWe conducted a comprehensive literature search using established Applied Health Sciences and Computer Science databases. Two independent researchers screened and selected the studies based on a systematic procedure. Inclusion criteria were as follows: participation was an explicit study aim or outcome or the targeted focus of the AI application; AI was applied as part of the provided and tested intervention; children or youth with a disability or other diagnosed health conditions were the focus of either the study or AI application or both; and the study was published in English. Data were mapped according to the types of AI, the mode of delivery, the type of personalization, and whether the intervention addressed individual goal-setting. ResultsThe literature search identified 3029 documents, of which 94 met the inclusion criteria. Most of the included studies used multiple applications of AI with the highest prevalence of robotics (72/94, 77%) and human-machine interaction (51/94, 54%). Regarding mode of delivery, most of the included studies described an intervention delivered in-person (84/94, 89%), and only 11% (10/94) were delivered remotely. Most interventions were tailored to groups of individuals (93/94, 99%). Only 1% (1/94) of interventions was tailored to patients’ individually reported participation needs, and only one intervention (1/94, 1%) described individual goal-setting as part of their therapy process or intervention planning. ConclusionsThere is an increasing amount of research on interventions using AI to target the participation of children and youth with disabilities or other diagnosed health conditions, supporting the potential of using AI in pediatric rehabilitation. On the basis of our results, 3 major gaps for further research and development were identified: a lack of remotely delivered participation-focused interventions using AI; a lack of individual goal-setting integrated in interventions; and a lack of interventions tailored to individually reported participation needs of children, youth, or families