Depression in left-behind adolescents from single-parent families: a nomogram based on multidimensional risk factors
Abstract Background Depression is a significant issue affecting adolescents’ mental health. While depression research is relatively extensive, studies focusing on left-behind adolescents from single-parent families remain limited. Due to their unique family structure, this group is more susceptible...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
|
| Series: | Child and Adolescent Psychiatry and Mental Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13034-025-00894-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Background Depression is a significant issue affecting adolescents’ mental health. While depression research is relatively extensive, studies focusing on left-behind adolescents from single-parent families remain limited. Due to their unique family structure, this group is more susceptible to multiple stressors, increasing their risk of depression. Objective This study aims to construct a predictive model based on a nomogram to identify the multidimensional characteristics of depression risk among left-behind adolescents from single-parent families, providing theoretical and practical evidence for early screening and targeted mental health interventions. Methods Cross-sectional data from the China Psychological Health Guardian Project (CPHG) were utilized to select samples of left-behind adolescents aged 12–20 years from single-parent families (N = 3731). Key variables were identified using Lasso regression, followed by the optimization of the model through binary logistic regression. A nomogram prediction model was then constructed based on significant variables. Results The study identified gender, age, duration of parental separation, family satisfaction, parental education levels, substance dependence, weekday sleep duration, weekend mobile phone use duration, and chronic diseases as key predictors of depression risk. The nomogram model demonstrated good discriminatory and predictive accuracy, with AUC values of 0.771 and 0.759 in the training and validation sets, respectively. Conclusion By integrating multidimensional variables, this study developed a predictive model for depression risk among left-behind adolescents from single-parent families, providing scientific evidence for the early identification and intervention of high-risk individuals. |
|---|---|
| ISSN: | 1753-2000 |