BACKGROUND: Attachment research has been limited by the lack of quick and easy measures. We report development and validation of the School Attachment Monitor (SAM), a novel measure for largescale assessment of attachment in children aged 5-9, in the general population. SAM offers automatic presentation, on computer, of story-stems based on the Manchester Child Attachment Story Task (MCAST), without the need for trained administrators. SAM is delivered by novel software which interacts with child participants, starting with warm-up activities to familiarise them with the task. Children's story completion is video recorded and augmented by 'smart dolls' that the child can hold and manipulate, with movement sensors for data collection. The design of SAM was informed by children of users' age range to establish their task understanding and incorporate their innovative ideas for improving SAM software. METHODS: 130 5-9 year old children were recruited from mainstream primary schools. In Phase 1, sixty-one children completed both SAM and MCAST. Inter-rater reliability and rating concordance was compared between SAM and MCAST. In Phase 2, a further 44 children completed SAM complete and, including those children completing SAM in Phase 1 (total n = 105), a machine learning algorithm was developed using a "majority vote" procedure where, for each child, 500 non-overlapping video frames contribute to the decision. RESULTS: Using manual rating, SAM-MCAST concordance was excellent (89% secure versus insecure; 97% organised versus disorganised; 86% four-way). Comparison of human ratings of SAM versus the machine learning algorithm showed over 80% concordance. CONCLUSIONS: We have developed a new tool for measuring attachment at the population level, which has good reliability compared to a validated attachment measure and has the potential for automatic rating-opening the door to measurement of attachment in large populations.
BACKGROUND: Attachment research has been limited by the lack of quick and easy measures. We report development and validation of the School Attachment Monitor (SAM), a novel measure for largescale assessment of attachment in children aged 5-9, in the general population. SAM offers automatic presentation, on computer, of story-stems based on the Manchester Child Attachment Story Task (MCAST), without the need for trained administrators. SAM is delivered by novel software which interacts with childparticipants, starting with warm-up activities to familiarise them with the task. Children's story completion is video recorded and augmented by 'smart dolls' that the child can hold and manipulate, with movement sensors for data collection. The design of SAM was informed by children of users' age range to establish their task understanding and incorporate their innovative ideas for improving SAM software. METHODS: 130 5-9 year old children were recruited from mainstream primary schools. In Phase 1, sixty-one children completed both SAM and MCAST. Inter-rater reliability and rating concordance was compared between SAM and MCAST. In Phase 2, a further 44 children completed SAM complete and, including those children completing SAM in Phase 1 (total n = 105), a machine learning algorithm was developed using a "majority vote" procedure where, for each child, 500 non-overlapping video frames contribute to the decision. RESULTS: Using manual rating, SAM-MCAST concordance was excellent (89% secure versus insecure; 97% organised versus disorganised; 86% four-way). Comparison of human ratings of SAM versus the machine learning algorithm showed over 80% concordance. CONCLUSIONS: We have developed a new tool for measuring attachment at the population level, which has good reliability compared to a validated attachment measure and has the potential for automatic rating-opening the door to measurement of attachment in large populations.
Authors: Barry Wright; Melissa Barry; Ellen Hughes; Dominic Trépel; Shehzad Ali; Victoria Allgar; Lucy Cottrill; Steven Duffy; Jenny Fell; Julie Glanville; Danya Glaser; Lisa Hackney; Laura Manea; Dean McMillan; Stephen Palmer; Vivien Prior; Clare Whitton; Amanda Perry; Simon Gilbody Journal: Health Technol Assess Date: 2015-07 Impact factor: 4.014
Authors: Helen Minnis; Warren Read; Brenda Connolly; Amanda Burston; Tanja-Sabine Schumm; Suren Putter-Lareman; Jonathan Green Journal: Int J Methods Psychiatr Res Date: 2010-08-26 Impact factor: 4.035
Authors: Annabel Futh; Thomas G O'Connor; Carla Matias; Jonathan Green; Stephen Scott Journal: J Am Acad Child Adolesc Psychiatry Date: 2008-06 Impact factor: 8.829