University of Hertfordshire

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The School Attachment Monitor—A novel computational tool for assessment of attachment in middle childhood. / Rooksby, Maki ; Folco, Simona Di ; Tayarani, Mohammad; Vo, Dong-Bach ; Huan, Rui ; Vinciarelli, Alessandro ; Brewster, Stephen A. ; Minnis, Helen.

In: PLoS ONE, Vol. 16, No. 7, e0240277, 22.07.2021.

Research output: Contribution to journalArticlepeer-review

Harvard

Rooksby, M, Folco, SD, Tayarani, M, Vo, D-B, Huan, R, Vinciarelli, A, Brewster, SA & Minnis, H 2021, 'The School Attachment Monitor—A novel computational tool for assessment of attachment in middle childhood', PLoS ONE, vol. 16, no. 7, e0240277. https://doi.org/10.1371/journal.pone.0240277

APA

Rooksby, M., Folco, S. D., Tayarani, M., Vo, D-B., Huan, R., Vinciarelli, A., Brewster, S. A., & Minnis, H. (2021). The School Attachment Monitor—A novel computational tool for assessment of attachment in middle childhood. PLoS ONE, 16(7), [e0240277]. https://doi.org/10.1371/journal.pone.0240277

Vancouver

Author

Rooksby, Maki ; Folco, Simona Di ; Tayarani, Mohammad ; Vo, Dong-Bach ; Huan, Rui ; Vinciarelli, Alessandro ; Brewster, Stephen A. ; Minnis, Helen. / The School Attachment Monitor—A novel computational tool for assessment of attachment in middle childhood. In: PLoS ONE. 2021 ; Vol. 16, No. 7.

Bibtex

@article{93fc5d404bcd4ab4a2528d86ecc1b093,
title = "The School Attachment Monitor—A novel computational tool for assessment of attachment in middle childhood",
abstract = "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{\textquoteright}s story completion is video recorded and augmented by {\textquoteleft}smart dolls{\textquoteright} that the child can hold and manipulate, with movement sensors for data collection. The design of SAM was informed by children of users{\textquoteright} 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.",
keywords = "Child, Child Behavior/physiology, Child, Preschool, Female, Humans, Machine Learning, Male, Object Attachment, Reproducibility of Results, Software",
author = "Maki Rooksby and Folco, {Simona Di} and Mohammad Tayarani and Dong-Bach Vo and Rui Huan and Alessandro Vinciarelli and Brewster, {Stephen A.} and Helen Minnis",
note = "{\textcopyright} 2021 Rooksby et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.",
year = "2021",
month = jul,
day = "22",
doi = "10.1371/journal.pone.0240277",
language = "English",
volume = "16",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "7",

}

RIS

TY - JOUR

T1 - The School Attachment Monitor—A novel computational tool for assessment of attachment in middle childhood

AU - Rooksby, Maki

AU - Folco, Simona Di

AU - Tayarani, Mohammad

AU - Vo, Dong-Bach

AU - Huan, Rui

AU - Vinciarelli, Alessandro

AU - Brewster, Stephen A.

AU - Minnis, Helen

N1 - © 2021 Rooksby et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.

PY - 2021/7/22

Y1 - 2021/7/22

N2 - 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.

AB - 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.

KW - Child

KW - Child Behavior/physiology

KW - Child, Preschool

KW - Female

KW - Humans

KW - Machine Learning

KW - Male

KW - Object Attachment

KW - Reproducibility of Results

KW - Software

UR - http://www.scopus.com/inward/record.url?scp=85111064096&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0240277

DO - 10.1371/journal.pone.0240277

M3 - Article

C2 - 34292952

VL - 16

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 7

M1 - e0240277

ER -