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Evaluating the dose-response relationship of the number of sessions of "It Takes Two to Talk®" in young children with language delay.

Shaza ZulkifliKate ShortCarissa KleimanJoanna C KiddJessica EarleySara BeckettJoseph DescallarPatricia J McCabe
Published in: International journal of speech-language pathology (2022)
Purpose: To evaluate the dose-response relationship between the number of It Takes Two to Talk ® (ITTT) sessions attended and the language outcomes of young children with language delay and their parent's responsivity in a multicultural clinical population. Method : A clinical caseload of 273 early language delayed children (mean age 29.2 months, SD 7.8) and their families participated in parent group workshops and individual coaching sessions of the parent responsivity program ITTT. The children's vocabulary and early syntax, collected using the MacArthur-Bates Communicative Development Inventories and mean length of the three longest utterances respectively, were collated from pre- and post-intervention from pre-existing clinical data. Parental responsivity was evaluated utilising the Parent-Child Interaction checklist at three time points. Multilevel regression was used to determine the relationship between the number of sessions attended and outcomes, while accounting for covariates such as age and language spoken. Result: ITTT dosage did not predict child language outcomes. Rather, vocabulary and early syntax outcomes were predicted by age, pre-scores and parent responsivity at the beginning of treatment. A higher dosage of ITTT did however positively predict parent responsivity, as did speaking only English at home. Socioeconomic status, gender and presence of receptive language difficulties did not contribute significantly to either child or parent outcomes. Conclusion : A lower dosage of the intervention may be considered for parents and children with fewer known risk factors without significant implications.
Keyphrases
  • autism spectrum disorder
  • randomized controlled trial
  • young adults
  • mental health
  • risk factors
  • type diabetes
  • machine learning
  • big data
  • artificial intelligence
  • combination therapy