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

Shahin, Saeideh; Rezze, Briano Di; Ahmed, Sara; Anaby, Dana

doi:10.1080/09638288.2022.2030809

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…Alternatively, non-AI participation assessments are often used within pediatric re/habilitation to gather self-or proxy-reported data for individual goal setting. For example, the Participation and Environment Measures (PEM) (63)(64)(65) assess for how often a child, youth or young adult participates in home, (pre-)school/daycare/work and community activities, their level of involvement in those activities, the desire for participation to change, applied participation-focused strategies, and the perceived impact of the environment on child, youth or young adult participation. Applications of AI such as recommender algorithms (e.g., constraint satisfaction and optimization) or NLP might provide simplified, more practical and lowcost ways for self-or proxy-reported data collection and interpretation for individual goal setting such as by systematically integrating responses into the individual child or youth participation profile, their participation goal, and intervention planning (12,16,66,67).…”

Section: Lack Of Ai-based Participation Assessment Approaches Integra...mentioning

confidence: 99%

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

Kaelin

Valizadeh

Salgado

et al. 2022

Front. Rehabilit. Sci.

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BackgroundThere 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.ObjectiveTo understand how the construct of participation is captured and operationalized in pediatric re/habilitation using AI.MethodsWe 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.ResultsTwenty 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.ConclusionsMain 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.

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Section: Lack Of Ai-based Participation Assessment Approaches Integra...mentioning

confidence: 99%

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

Kaelin

Valizadeh

Salgado

et al. 2022

Front. Rehabilit. Sci.

Self Cite

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

Shahin

Ahmed

Rezze

et al. 2023

Physical & Occupational Therapy In Pediatrics

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

Cited by 2 publications

References 45 publications

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

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

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

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