SAM: Automated Attachment Analysis Using the School Attachment Monitor

Lead Research Organisation: University of Glasgow
Department Name: School of Computing Science

Abstract

Our aim in SAM is to develop a computer-based tool which can measure parent-child Attachment across the population in a cost-effective way. The National Children's Bureau states that "secure attachment promotes health and wellbeing" while the Early Childhood Forum advocates "the right of children to [...] form secure, long lasting attachment relationships [...] which shape their future capacities for wellbeing". When the problem is neglected, the consequences are dire: children who have abnormal family attachments are at much higher risk of aggressive behaviours. By early adulthood, individuals with aggressive behaviour cost society 10 times more than their peers and have a mortality rate almost 10 times higher, in part due to increased risk of suicide and violent behaviour, but also due to physical problems such as coronary heart pathologies. Identifying Attachment problems early, at a population level, would be of significant benefit to society and drastically reduce the costs of dealing with the resulting issues.

Large-scale screenings of Attachment insecurity should be routine among children. The problem is that Attachment assessment methods are expensive and time-consuming. MCAST (Manchester Child Attachment Story Task) is the standard method used in middle childhood. During MCAST administration, assessors show vignettes to the child, using a dolls-house, which portray mildly stressful situations. They are then asked to act out what happens in the rest of the story using dolls that represent both the child and a caregiver. The way the child completes the story and their behaviour during the test provides the cues necessary to assess their Attachment status. Each MCAST takes 30 minutes to administer and a further two hours to be transformed into a usable medical record. Furthermore, professionals must attend expensive courses followed by lengthy reliability training to use MCAST, so accredited Attachment assessors are a rare commodity. This means that MCAST cannot be applied on a large scale, as needed to make a significant impact on population health and wellbeing.

Our goal is to make large-scale Attachment screening possible by reducing time and costs required for MCAST assessment. Our approach consists of automating the key steps of MCAST to 1) reduce the time needed to complete the test (higher efficiency) and, 2) allow the involvement of personnel with no MCAST training (lower costs). We also expect the automation of MCAST to provide new insights into Attachment and its observable, machine detectable behavioural markers, enabling better future measurement of Attachment.

We will develop a computer-based tool which can be used to measure Attachment across the population in a rapid, cost-effective way to support MCAST assessors. The children will be guided through the story vignettes by an on-screen avatar. The detailed movements and positions of the dolls in space will be captured in real time. We will also record speech sounds from the children to analyse prosody and vocalisations. Using these data, we will develop novel algorithms to categorise Attachment patterns automatically and rapidly, locating each child in one of the four Attachment categories (Secure; Insecure Resistant-Ambivalent; Insecure Avoidant and Insecure Disorganised/Disorientated) with a level of confidence. To do this, we will develop novel techniques based on Social Signal Processing (SSP), in which Vinciarelli is a leading expert

With SAM, the screening sessions and preliminary data analysis can be done without the presence of trained MCAST assessors; they would only be needed if a child was tagged as being in one of the problem categories, where a standard MCAST assessment would be undertaken, allowing large-scale population screening of Attachment patterns for the first time. The development of SAM and the rapid screening of Attachment in large groups will create a paradigm shift in the treatment of child psychiatric disorders.

Planned Impact

There are four principal groups of beneficiaries for social and economic impact:

* Society - The costs to society due to poor Attachment are very high. Detecting problems early by facilitating population screening will allow rapid treatment, meaning great savings in healthcare costs, reductions in violence, etc;

* Health and social policy makers - with SAM it will be possible for policy makers to map geographical areas by Attachment status and so target resources to deal with problems in an informed way for the first time. Being able to measure Attachment at the population level will mean that, over the long term, policies within a city or country can significantly reduce the problems caused by insecure Attachment;

* MCAST Assessors - The number of trained assessors is limited and assessments take a long time. This means that it is not possible to screen the population of children for Attachment problems. SAM will enable this group to become much more efficient. They will be able to assess many more children without increasing costs;

* Children's charities / Parents associations - Charities, such as the NSPCC, spend a lot of resources taking care of children who have poor Attachment and its resulting problems. If these problems can be dealt with early then they can use their limited resources to tackle other problems that blight the lives of children.

The problems caused by insecure Attachment impose a significant cost on society. Children who have abnormal family attachments are at much higher risk of aggressive behaviours. By early adulthood, individuals with aggressive behaviour cost society 10 times more than their peers and have a mortality rate almost 10 times higher, in part due to increased risk of suicide and violent behaviour, but also due to physical problems such as coronary heart pathologies. If we can screen the population to identify individuals with poor Attachment early on, they can be treated and save health and social services large amounts of time and resources.

The recently published European Union Roadmap for the Future of Child Health Research (www.childhealth research.eu) has stated that "Determining how [mental health] can be measured adequately in [young children] to enable identification (screening) of those with good mental health vs. those who are at risk for poor mental health is an important task for the research community... Early detection of problem areas is crucial, and therefore, it is essential that monitoring systems are established based on sound indicators...". The National Institute for Health and Care Excellence (NICE) has recognised the importance of Attachment by commissioning a NICE Guideline on it (https://www.nice.org.uk/Guidance/InDevelopment/GID-CGWAVE0675) due to report in 2015. There are no good methods of measuring Attachment across populations at present: the SAM project will provide the tools to do this.

Publications


10 25 50
 
Description We are working on a way to automatically classify parent/child attachment. We are now in the middle of the data capture phase to build models of Attachment
Exploitation Route others can use our system (if we are successful) to classify Attachment across large populations
Sectors Healthcare
 
Description NSPCC 
Organisation National Society for the Prevention of Cruelty to Children (NSPCC)
Country United Kingdom of Great Britain & Northern Ireland (UK) 
Sector Charity/Non Profit 
PI Contribution We are doing the research in the SAM project but NSPCC provide input to direct our research
Collaborator Contribution the key role of NSPCC is to commercialise the results of the work at the end of the project
Impact none yet
Start Year 2015