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Purpose: To examine (1) how much participation is represented in the benchmark Unified Medical Language System (UMLS) resource, and (2) to what extent that representation reflects the definition of child and youth participation and/or its related constructs per the family of Participation-Related Constructs framework.

Materials and methods: We searched and analysed UMLS concepts related to the term “participation.” Identified UMLS concepts were rated according to their representation of participation (i.e., attendance, involvement, both) as well as participation-related constructs using deductive content analysis.

Results: 363 UMLS concepts were identified. Of those, 68 had at least one English definition, resulting in 81 definitions that were further analysed. Results revealed 2 definitions (2/81; 3%; 2/68 UMLS concepts) representing participation “attendance” and 18 definitions (18/81; 22%; 14/68 UMLS concepts) representing participation “involvement.” No UMLS concept definition represented both attendance and involvement (i.e., participation). Most of the definitions (11/20; 55%; 9/16 UMLS concepts) representing attendance or involvement also represent a participation-related construct.

Conclusion(s): The representation of participation within the UMLS is limited and poorly aligned with the contemporary definition of child and youth participation. Expanding ontological resources to represent child and youth participation is needed to enable better data analytics that reflect contemporary paediatric rehabilitation practice.

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. 

Caregiver strategy classification in pediatric rehabilitation contexts is strongly motivated by real-world clinical constraints but highly underresourced and seldom studied in natural language processing settings. We introduce a large dataset of 3,062 caregiver strategies in this setting, a five-fold increase over the nearest contemporary dataset. These strategies are manually categorized into clinically established constructs with high agreement (κ=0.68-0.89). We also propose two techniques to further address identified data constraints. First, we manually supplement target task data with relevant public data from online child health forums. Next, we propose a novel data augmentation technique to generate synthetic caregiver strategies with high downstream task utility. Extensive experiments showcase the quality of our dataset. They also establish evidence that both the publicly available data and the synthetic strategies result in large performance gains, with relative F1 increases of 22.6% and 50.9%, respectively.

Introduction: The purpose of this mixed-methods study is to examine the role of caregiver strategies to support community participation among children and youth with disabilities and those at risk, from the caregiver perspective. For the quantitative phase, we tested the hypothesized positive effect of participation-focused caregiver strategies on the relationship(s) between participation-related constructs and community participation attendance and involvement. For the qualitative phase, we solicited caregiver perspectives to explain the quantitative findings.

Methods: An explanatory sequential mixed-methods design (QUAN > qual) was used. For the quantitative phase, we conducted secondary analyses of data collected during a second follow-up phase of a longitudinal cohort study, including 260 families of children and youth (mean age: 13.5 years) with disabilities and those at risk [i.e., 120 families of children and youth with craniofacial microsomia (CFM); 140 families of children and youth with other types of childhood-onset disabilities]. Data were collected through the Participation and Environment Measure—Children and Youth, the Pediatric Quality of Life Inventory, and the Child Behavior Checklist and analyzed using structural equation modeling. For the qualitative phase, we conducted semi-structured interviews with eight caregivers of children and youth with disabilities and those at risk (i.e., three caregivers of children and youth with CFM; five caregivers of children and youth with other childhood-onset disabilities). Interviews were transcribed verbatim and inductively content-analyzed.

Results: Our model reached acceptable to close model fit [CFI = 0.952; RMSEA = 0.068 (90% CI = 0.054–0.082); SRMR = 0.055; TLI = 0.936], revealing no significant effect of the number of participation-focused caregiver strategies on the relationships between participation-related constructs (e.g., activity competence, environment/context) and community participation in terms of attendance and involvement. The qualitative findings revealed three main categories for how caregivers explained these quantitative results: (1) caregiver workload and supports needed for implementing strategies; (2) caregivers careful strategy quality appraisal; and (3) community setting characteristics hindering successful strategy implementation.

Discussion: The findings suggest that the insignificant effect of the number of caregiver strategies may be explained by the intensified need for caregiver effort and support to develop and implement quality strategies that are responsive to community setting characteristics.

Introduction: Human-centric artificial intelligence (HCAI) focuses on systems that support and collaborate with humans to achieve their goals. To better understand how collaboration develops in human-AI teaming, further exploration grounded in a theoretical model is needed. Tuckman's model describes how team development among humans evolves by transitioning through the stages of forming, storming, norming, performing, and adjourning. The purpose of this pilot study was to explore transitions between the first three stages in a collaborative task involving a human and a human-centric agent.

Method: The collaborative task was selected based on commonly performed tasks in a therapeutic healthcare context. It involved planning activities for the upcoming week to achieve health-related goals. A calendar application served as a tool for this task. This application embedded a collaborative agent designed to interact with humans following Tuckman's stages of team development. Eight participants completed the collaborative calendar planning task, followed by a semi-structured interview. Interviews were transcribed and analyzed using inductive content analysis.

Results: The results revealed that the participants initiated the storming stage in most cases (n = 7/8) and that the agent initiated the norming stage in most cases (n = 5/8). Additionally, three main categories emerged from the content analyses of the interviews related to participants' transition through team development stages: (i) participants' experiences of Tuckman's first three stages of team development; (ii) their reactions to the agent's behavior in the three stages; and (iii) factors important to the participants to team up with a collaborative agent.

Conclusion: Results suggest ways to further personalize the agent to contribute to human-agent teamwork. In addition, this study revealed the need to further examine the integration of explicit conflict management into human-agent collaboration for human-agent teamwork.

Background: Artificial intelligence (AI) technology is constantly and rapidly evolving and has the potential to benefit occupational therapy (OT) and OT clients. However, AI developments also pose risks and challenges, for example in relation to the ethical principles of OT. One way to support future AI technology aligned with OT ethical principles may be through human-centered AI (HCAI), an emerging branch within AI research and developments with a notable overlap of OT values and beliefs.

Objective: To explore the risks and challenges of AI technology, and how the combined expertise, skills, and knowledge of OT and HCAI can contribute to harnessing its potential and shaping its future, from the perspective of OT’s ethical values and beliefs.

Results: Opportunities for OT and HCAI collaboration related to future AI technology include ensuring a focus on 1) occupational performance and participation, while taking client-centeredness into account; 2) occupational justice and respect for diversity, and 3) transparency and respect for the privacy of occupational performance and participation data.

Conclusion and Significance: There is need for OTs to engage and ensure that AI is applied in a way that serves OT and OT clients in a meaningful and ethical way through the use of HCAI.

Aim: To examine how school environment, physical functioning problems, and behavioral problems explain levels of school participation (i.e. attendance and involvement) among young people with craniofacial microsomia (CFM) and other childhood-onset disabilities, and whether participation-focused caregiver strategies play a role in these relationships.

Method: We conducted secondary analyses of a subset of data (n = 260 families: 120 with CFM and 140 with other childhood-onset disabilities) from the second follow-up phase of a longitudinal cohort study. We applied structural equation modeling with data collected from the Participation and Environment Measure – Children and Youth version, the Child Behavior Checklist, and the Pediatric Quality of Life Inventory physical functioning scale.

Results: Model fit was acceptable to close (comparative fit index = 0.973; root mean square error of approximation = 0.055; standardized root mean squared residual = 0.043; Tucker–Lewis index = 0.958). School environmental support had a positive effect on young people's participation attendance and involvement, and physical functioning problems had a negative effect on participation involvement. The number of disclosed caregiver strategies had a significant positive effect on the relationship between school environmental support and school participation attendance.

Interpretation: Findings confirm the effect of school environmental support and physical functioning problems on school participation and highlight the role of participation-focused caregiver strategies to intensify the positive effect of school environmental support on school participation attendance.

Health-promoting digital agents, taking on the role of an assistant, coach or companion, are expected to have knowledge about a person's medical and health aspects, yet they typically lack knowledge about the person's activities. These activities may vary daily or weekly and are contextually situated, posing challenges for the human-Agent interaction. This pilot study aimed to explore the experiences and behaviors of older adults when interacting with an initially unknowledgeable digital agent that queries them about an activity that they are simultaneously engaged in. Five older adults participated in a scenario involving preparing coffee followed by having coffee with a guest. While performing these activities, participants educated the smartwatch-embedded agent, named Virtual Occupational Therapist (VOT), about their activity performance by answering a set of activity-ontology based questions posed by the VOT. Participants' interactions with the VOT were observed, followed by a semi-structured interview focusing on their experience with the VOT. Collected data were analyzed using an activity-Theoretical framework. Results revealed participants exhibited agency and autonomy, deciding whether to adapt to the VOT's actions in three phases: Adjustment to the VOT, partial adjustment, and the exercise of agency by putting the VOT to sleep after the social conditions and activity changed. Results imply that the VOT should incorporate the ability to distinguish when humans collaborate as expected by the VOT and when they choose not to comply and instead act according to their own agenda. Future research focuses on how collaboration evolves and how the VOT needs to adapt in the process.

Objective: The aim of the study is to identify and prioritize early intervention (EI) stakeholders' perspectives of supports and barriers to implementing the Young Children's Participation and Environment Measure (YC-PEM), an electronic patient-reported outcome (e-PRO) tool, for scaling its implementation across multiple local and state EI programs.

Methods: An explanatory sequential (quan > QUAL) mixed-methods study was conducted with EI families (n = 6), service coordinators (n = 9), and program leadership (n = 7). Semi-structured interviews and focus groups were used to share select quantitative pragmatic trial results (e.g., percentages for perceived helpfulness of implementation strategies) and elicit stakeholder perspectives to contextualize these results. Three study staff deductively coded transcripts to constructs in the Consolidated Framework for Implementation Research (CFIR). Data within CFIR constructs were inductively analyzed to generate themes that were rated by national early childhood advisors for their relevance to longer term implementation.

Results: All three stakeholder groups (i.e., families, service coordinators, program leadership) identified thematic supports and barriers across multiple constructs within each of four CFIR domains: (1) Six themes for intervention characteristics, (2) Six themes for process, (3) three themes for inner setting, and (4) four themes for outer setting. For example, all stakeholder groups described the value of the YC-PEM e-PRO in forging connections and eliciting meaningful information about family priorities for efficient service plan development (intervention characteristics). Stakeholders prioritized reaching families with diverse linguistic preferences and user navigation needs, further tailoring its interface with automated data capture and exchange processes (process); and fostering a positive implementation climate (inner setting). Service coordinators and program leadership further articulated the value of YC-PEM e-PRO results for improving EI access (outer setting).

Conclusion: Results demonstrate the YC-PEM e-PRO is an evidence-based intervention that is viable for implementation. Optimizations to its interface are needed before undertaking hybrid type-2 and 3 multisite trials to test these implementation strategies across state and local EI programs with electronic data capture capabilities and diverse levels of organizational readiness and resources for implementation.