Evaluating child–caregiver attachment beyond infancy when language barriers exist

Abstract

Observational coding systems are invaluable for understanding child–caregiver interactions, but their reliance on language comprehension limits their use with linguistically diverse populations. We developed the Behavioral Recognition in Displaced Group Exchanges (BRIDGE) coding system to measure the quality of refugee child–caregiver relationships through paralinguistic cues (e.g., tone) and kinetic behavioral signals (e.g., gestures). Here BRIDGE was tested on 1,092 videos featuring 287 Syrian refugee mothers and their children in Jordan (Mage = 6, range 4–8; 1:1 gender ratio). Most coders were unable to understand the language the dyads spoke, yet all demonstrated excellent intra- and interrater reliability across preregistered analyses. Coding patterns also showed convergence, divergence, sensitivity to contextual factors, and temporal stability, establishing BRIDGE as a robust linguistically agnostic tool.

Lay Summary
Understanding how well caregivers and school-age children interact usually requires knowing the language they speak. We created Behavioral Recognition in Displaced Group Exchanges (BRIDGE), a tool that measures the quality of child–caregiver relationships beyond infancy using tone of voice and body language instead of words. Here BRIDGE was tested on a large set of videos featuring Syrian refugee mothers and their school-age children in Jordan, which were rated by observers from around the world. Even though most observers could not understand Arabic, they were still able to reliably judge the relationships, showing that BRIDGE works across language barriers.