@inproceedings{valizadeh-etal-2024-carecorpus,
    title = "{C}are{C}orpus: A Corpus of Real-World Solution-Focused Caregiver Strategies for Personalized Pediatric Rehabilitation Service Design",
    author = "Valizadeh, Mina  and
      Kaelin, Vera C.  and
      Khetani, Mary A.  and
      Parde, Natalie",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.256",
    pages = "2871--2882",
    abstract = "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.",
}

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.
Valizadeh, Mina Kaelin, Vera C. Khetani, Mary A. Parde, Natalie
CareCorpus: A Corpus of Real-World Solution-Focused Caregiver Strategies for Personalized Pediatric Rehabilitation Service Design