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This research aimed at applying natural language processing to develop and identify a best performing predictive model that classifies caregiver strategies into participation-related constructs, while filtering out non-strategies. We created a dataset including 1,576 caregiver strategies obtained from 236 families of children and youth (11\u201317 years) with craniofacial microsomia or other childhood-onset disabilities. These strategies were annotated to four participation-related constructs and a non-strategy class. We experimented with manually created features (i.e., speech and dependency tags, predefined likely sets of words, dense lexicon features (i.e., Unified Medical Language System (UMLS) concepts)) and three classical methods (i.e., logistic regression, na\u00efve Bayes, support vector machines (SVM)). We tested a series of binary and multinomial classification tasks applying 10-fold cross-validation on the training set (80%) to test the best performing model on the held-out test set (20%). SVM using term frequency-inverse document frequency (TF-IDF) was the best performing model for all four classification tasks, with accuracy ranging from 78.10 to 94.92% and a macro-averaged F1-score ranging from 0.58 to 0.83. Manually created features only increased model performance when filtering out non-strategies. Results suggest pipelined classification tasks (i.e., filtering out non-strategies; classification into intrinsic and extrinsic strategies; classification into participation-related constructs) for implementation into participation-focused pediatric rehabilitation interventions like Participation and Environment Measure Plus (PEM+) among caregivers who complete the Participation and Environment Measure for Children and Youth (PEM-CY).<\/jats:p>","DOI":"10.1007\/s41666-023-00149-y","type":"journal-article","created":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T15:02:06Z","timestamp":1695049326000},"page":"480-500","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Natural Language Processing to Classify Caregiver Strategies Supporting Participation Among Children and Youth with Craniofacial Microsomia and Other Childhood-Onset Disabilities"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1290-9441","authenticated-orcid":false,"given":"Vera C.","family":"Kaelin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3459-9379","authenticated-orcid":false,"given":"Andrew D.","family":"Boyd","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3392-6814","authenticated-orcid":false,"given":"Martha M.","family":"Werler","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0072-7499","authenticated-orcid":false,"given":"Natalie","family":"Parde","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2628-3371","authenticated-orcid":false,"given":"Mary A.","family":"Khetani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,18]]},"reference":[{"key":"149_CR1","unstructured":"Eunice Kennedy Shriver National Institute of Child Health and Human Development and the NIH Medical Rehabilitation Coordinating Committee (2020): National Institutes of Health (NIH) research plan on rehabilitation. https:\/\/nichd.ideascalegov.com\/a\/campaign-home\/51.\u00a0 Accessed 21 Jan 2021"},{"key":"149_CR2","unstructured":"(2011) European Agency for Development in Special Needs Education: Key principles for promoting quality in inclusive education. 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Written informed consent was obtained by all participants for the original project.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"N\/A.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}