School-Age Assessment of Attachment: an analysis of non-verbal behavior using Dr. Notes.

Procedures to assess attachment in school-age are mainly based on verbal responses and are carried out in conditions of relative security, implying some reliability and validity problems. In particular, the assessment in these children presents particular difficulties related to the specific characteristics of this stage of development—such as the different use of verbal and non-verbal communication and the particular display of facial expressions and behaviors related to their inner emotional states. The School-age Assessment of Attachment (SAA) (Crittenden, 1997-2005) is a semi-structured clinical interview that assesses self-protective strategies in children from six to thirteen years following the Dynamic-Maturational Model of attachment and adaptation (DMM). The procedure consists of a set of cards regarding increasing levels of threats that school-age children frequently face. The interview is usually audio recorded, and the classification of the transcript is based on the observation of the child’s behavior and a discourse analysis derived from the Adult Attachment Interview. Adding the video to the transcript improves the classification process by benefiting from para-verbal and non-verbal information— such as the display of false-positive affects in dismissing (Type A) children or the emphasis of negative feelings in preoccupied/coercive (Type C) children, and somatic response. An example of a videotaped SAA of a clinical case will be displayed using Doctor Notes® (Telpress International, 2015), an application originally developed for audiovisual analysis and classification of information that can provide specific tags and a set of buttons for SAA coding. SAA represents a bridge between infant observational procedures and verbal interviews with adults, evidencing the utility of integrating a careful behavioral analysis with the coding process. Using Dr. Notes, the process of transcription based on the video analysis allows a more accurate detection of para-verbal and non-verbal behavior offering researchers and clinicians more information for the attachment classification.