Development and content validity of the youth and young-adult participation and environment measure (Y-PEM)

Reliability and Validity of the Youth and Young-Adult Participation and Environment Measure (Y-PEM): An Initial Evaluation

AIM

To examine psychometric properties and aspects of utility of the Youth and young-adult Participation and Environment Measure (Y-PEM).

METHODS

Young people with and without physical disabilities (n = 113) aged 12 to 31 (x¯ = 23; SD = 4.3) completed an online survey containing the Y-PEM and QQ-10 questionnaire. To examine construct validity, differences in participation levels and environmental barriers/facilitators were examined between those with (n = 56) and without disabilities (n = 57) via t-test. Internal consistency was computed using Cronbach's alpha. To examine test-retest reliability, a sub-sample of 70 participants completed the Y-PEM a second time, 2-4 weeks apart. The Intraclass correlation coefficient (ICC) was calculated.

RESULTS

Descriptively, participants with disabilities had lower levels of frequency and involvement across all four settings: home, school/educational, community, workplace. Internal consistency were 0.71 and above (up to 0.82) across all scales with the exception of home (0.52) and workplace frequency (0.61). Test-retest reliability were 0.70 and above (up to 0.85) across all settings except for environmental supports at school (0.66) and workplace frequency (0.43). Y-PEM was perceived as a valuable tool with relatively low burden.

CONCLUSIONS

Initial psychometric properties are promising. Findings support Y-PEM's use as a feasible self-reported questionnaire for individuals aged 12-30 years old.

Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review

Background

There is increased interest in using artificial intelligence (AI) to provide participation-focused pediatric re/habilitation. Existing reviews on the use of AI in participation-focused pediatric re/habilitation focus on interventions and do not screen articles based on their definition of participation. AI-based assessments may help reduce provider burden and can support operationalization of the construct under investigation. To extend knowledge of the landscape on AI use in participation-focused pediatric re/habilitation, a scoping review on AI-based participation-focused assessments is needed.

Objective

To understand how the construct of participation is captured and operationalized in pediatric re/habilitation using AI.

Methods

We conducted a scoping review of literature published in Pubmed, PsycInfo, ERIC, CINAHL, IEEE Xplore, ACM Digital Library, ProQuest Dissertation and Theses, ACL Anthology, AAAI Digital Library, and Google Scholar. Documents were screened by 2–3 independent researchers following a systematic procedure and using the following inclusion criteria: (1) focuses on capturing participation using AI; (2) includes data on children and/or youth with a congenital or acquired disability; and (3) published in English. Data from included studies were extracted [e.g., demographics, type(s) of AI used], summarized, and sorted into categories of participation-related constructs.

Results

Twenty one out of 3,406 documents were included. Included assessment approaches mainly captured participation through annotated observations (n = 20; 95%), were administered in person (n = 17; 81%), and applied machine learning (n = 20; 95%) and computer vision (n = 13; 62%). None integrated the child or youth perspective and only one included the caregiver perspective. All assessment approaches captured behavioral involvement, and none captured emotional or cognitive involvement or attendance. Additionally, 24% (n = 5) of the assessment approaches captured participation-related constructs like activity competencies and 57% (n = 12) captured aspects not included in contemporary frameworks of participation.

Conclusions

Main gaps for future research include lack of: (1) research reporting on common demographic factors and including samples representing the population of children and youth with a congenital or acquired disability; (2) AI-based participation assessment approaches integrating the child or youth perspective; (3) remotely administered AI-based assessment approaches capturing both child or youth attendance and involvement; and (4) AI-based assessment approaches aligning with contemporary definitions of participation.