ArticleBMC psychology2026
Network and machine learning analysis of childhood trauma, mental health, and AI-based emotional support needs in adolescents from underdeveloped regions.
Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundThe current intervention efficacy of generative conversational artificial intelligence (GCAI) on overall mental health issues remains limited, which may be related to the complex and unclear relation between GCAI emotional support needs (GCAI-ESN) and childhood trauma and mental health issues.
methodsWe used the Childhood Trauma Questionnaire, the Mental Health Inventory of Middle-school students, and whether there were GCAI-ESN to assess 14,380 adolescents. Machine learning (ML) combined with SHapley Additive exPlanations (SHAP) analysis was employed to identify the key predictors influencing the GCAI-ESN model. Based on the identified predictors, an undirected network was constructed and a Bayesian network analysis was conducted.
resultsThe prevalence of childhood trauma, mental health issues, and GCAI-ESN among adolescents in underdeveloped regions was 32.43% (95% CI: 31.66%-33.19%), 16.78% (95% CI: 16.17%-17.39%), and 39.53% (95% CI: 38.73%-40.33%), respectively. The SHAP analysis of four machine learning models identified 15 key predictors for GCAI-ESN. In the undirected network model, easily anxious 'MH6' (EI = 1.07) and depressed 'MH5' (EI = 1.07) are the core nodes, while GCAI-ESN (BEI = 3.09) and family said hurtful things 'CTQ14' (BEI = 1.00) are the bridge nodes. Bayesian network analysis indicates that 'CTQ14' is the node with the most outgoing potential predictive relationships.
conclusionsThis study identified the key predictors of GCAI-ESN among adolescents in underdeveloped regions and determined 'MH6', 'MH5', and 'CTQ14' as potential intervention targets when the GCAI provides emotional support, offering concrete focal points for future research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.