Supplements

Probable depression is prevalent among parents and primary caregivers of children with specific learning disorders in Saudi Arabia, and machine-learning models can predict it with high accuracy.

The study found that 35.6% of 612 caregivers met criteria for probable depression (PHQ-9 ≥ 10). XGBoost achieved an ROC AUC of 0.916, sensitivity of 0.858, and specificity of 0.830 at a screening threshold of 0.425, with key predictors including parenting stress, anxiety, sleep quality, and child difficulties.

Last updated: Jul 28, 20260 RCTs📖 Read as article →

Evidence Score

Evidence Score32/100
Human RCT☆☆☆☆☆
Meta-analysis☆☆☆☆☆
Mechanism★★★★★
Safety★★★★
Confidencelow

Study Evidence

Study 1. Exploring the Prevalence and Key Predictors of Probable Depression Among Parents and Primary Caregivers of Children and Adolescents With Specific Learning Disorders in the Kingdom of Saudi Arabia: A Cross-Sectional Explainable Machine-Learning Study.

observational

Almulla AA, Khasawneh MAS · Clinical psychology & psychotherapy (2026)

Participants: 612
Duration: cross-sectional
Intervention: Explainable machine-learning models (Elastic-net, Random Forest, XGBoost, LightGBM, soft-voting ensemble) for screening-oriented classification of probable depression
Outcome: Probable depression defined as PHQ-9 score ≥ 10, model performance (ROC AUC, average precision, Brier score, sensitivity, specificity, negative predictive value)
Effect Size: N/A
Population: 612 parents and primary caregivers of children and adolescents with specific learning disorders in Saudi Arabia, recruited through clinical, psychoeducational, educational, and community pathways

Result: The study found that 35.6% of 612 caregivers met criteria for probable depression (PHQ-9 ≥ 10). XGBoost achieved an ROC AUC of 0.916, sensitivity of 0.858, and specificity of 0.830 at a screening threshold of 0.425, with key predictors including parenting stress, anxiety, sleep quality, and child difficulties.

Mechanism Graph

Caregivers of children with SLDs experience high parenting stress and anxiety symptoms
Poor sleep quality and low perceived social support compound psychological burden
Child emotional/behavioral difficulties and SLD severity increase caregiver strain
Result: 35.6% of caregivers meet criteria for probable depression

Limitations

  • Cross-sectional design prevents causal inference
  • Sample recruited through service and community pathways may not be representative of all caregivers in Saudi Arabia
  • External validation of machine-learning models is required before clinical implementation

Frequently Asked Questions

What is the prevalence of probable depression among caregivers of children with SLDs in Saudi Arabia?

The study found a prevalence of 35.6% (95% CI 31.8-39.6) among 612 caregivers, based on a PHQ-9 score of 10 or higher.

Which machine-learning model performed best for predicting caregiver depression?

XGBoost and the soft-voting ensemble achieved the highest ROC AUC of 0.916. XGBoost had an average precision of 0.867 and a Brier score of 0.115.

What are the top predictors of probable depression in these caregivers?

Key predictors identified by SHAP analysis include parenting stress, parental anxiety symptoms, sleep quality, SLD severity, child emotional and behavioral difficulties, perceived social support, and coping capacity.

Can these models be used in clinical practice now?

No, the authors state that external evaluation is required before implementation, as the models were developed on a specific sample and need validation in other populations.

Products

Affiliate links coming soon. We only recommend products that match the doses and forms used in the cited research.

References

  1. 1.Almulla AA, Khasawneh MAS. "Exploring the Prevalence and Key Predictors of Probable Depression Among Parents and Primary Caregivers of Children and Adolescents With Specific Learning Disorders in the Kingdom of Saudi Arabia: A Cross-Sectional Explainable Machine-Learning Study.." Clinical psychology & psychotherapy, 2026. PMID: 42490731 DOI: 10.1002/cpp.70310
Disclaimer: This content is for educational purposes only and is not medical advice. Evidence scores reflect the quality and quantity of available research, not clinical recommendations. Always consult a healthcare professional before starting any supplement or intervention.