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Measuring child sexual exploitation and abuse in Armenian schools: Feasibility of a digital survey and implications of module-specific consent

Child Abuse Negl. 2026 Apr 24;176:108071. doi: 10.1016/j.chiabu.2026.108071. Online ahead of print.

ABSTRACT

BACKGROUND: Reliable self-report data on child sexual exploitation and abuse (CSEA) are limited in Armenia, where cultural taboos and disclosure barriers complicate school-based measurement.

OBJECTIVE: This feasibility study aimed (1) to assess the ethical and practical feasibility of administering a self-administered digital school survey including a sensitive CSEA module, and (2) to quantify module-specific consent, examine correlates of opting in, and describe consent-conditioned disclosure rates.

PARTICIPANTS AND SETTING: A stratified sample of 336 adolescents (12-17 years) from public schools across four Armenian regions, administered in 21 classroom sessions.

METHODS: Procedure included two-stage assent (parent/child) plus additional on-screen consent before the CSEA module, adapted ICAST-C/JVQ-R2 screeners with follow-ups, hierarchical logistic regression for correlates of consent, and facilitator field notes on implementation and safeguarding.

RESULTS: Additional consent to the sensitive module was obtained from 42.6% of participants (n = 143 of 336). Among students who consented to the sensitive module, girls constituted 70.6% of consenters and boys 26.6%, with a significant difference in the subgroup distribution (Pearson’s χ2(2) = 15.56, p < .001). Consent was associated with knowing a victim and with the breadth of school-based prevention exposure. Among consenters, 19 (13.3%) reported at least one CSEA experience. Field implementation was safe and workable.

CONCLUSIONS: A digital self-administered school survey on CSEA was feasible in Armenian classrooms under enhanced consent and safeguarding procedures. However, patterned module refusal indicates that future representative studies should treat module-specific consent as part of the measurement process and address nonrandom missingness when estimating prevalence.

PMID:42033960 | DOI:10.1016/j.chiabu.2026.108071

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