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Mother-Child Relations

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Mapping the Early Language Environment Using All-Day Recordings and Automated Analysis.

American journal of speech-language pathology
PURPOSE: This research provided a first-generation standardization of automated language environment estimates, validated these estimates against standard language assessments, and extended on previous research reporting language behavior differences...

Artificial neural network coding of the child attachment interview using linguistic data.

Attachment & human development
Assessing attachment in adolescents is important due to relations between insecurity and psychopathology. The child attachment interview (CAI) holds promise in this regard, but is time-consuming to code, which may render it inaccessible. The aim of t...

Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research.

Development and psychopathology
As early as infancy, caregivers' facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expr...

Machine learning approach to measurement of criticism: The core dimension of expressed emotion.

Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43)
Expressed emotion (EE), a measure of the family's emotional climate, is a fundamental measure in caregiving research. A core dimension of EE is the level of criticism expressed by the caregiver to the care recipient, with a high level of criticism a ...

Predicting mother and newborn skin-to-skin contact using a machine learning approach.

BMC pregnancy and childbirth
BACKGROUND: Despite the known benefits of skin-to-skin contact (SSC), limited data exists on its implementation, especially its influencing factors. The current study was designed to use machine learning (ML) to identify the predictors of SSC.