The role of family environment and parental factors: a person-oriented study of adolescents’ psychological distress and help-seeking patterns
Bibliographic record
Abstract
BACKGROUND: Adolescents' mental health is shaped by their coping strategies and the broader family context in which they live. However, few studies have examined psychological distress and help-seeking patterns jointly, especially from a person-oriented perspective. Understanding distinct adolescent risk profiles and how family and parental factors influence them may inform more effective prevention strategies. This study aimed to: (1) identify latent profiles of adolescents based on their psychological distress and help-seeking intentions; and (2) explore how family and parental factors predict profile membership and self-harm risk. METHODS: A cross-sectional study was conducted in 2021 with 7,934 Chinese secondary school students and one parent per adolescent. Adolescents completed validated measures of depression, anxiety, and help-seeking intentions; parents reported on family income, family function, mental health symptoms, and mental health stigma. Latent profile analysis and the BCH three-step method were used to identify subgroups and examine predictors and outcomes. RESULTS: Five profiles were identified: normative, safe, distress, high-risk, and aware. The high-risk profile (5.98%) showed high distress, low help-seeking, and the highest self-harm rate (48.7%). Lower family functioning and higher parental distress predicted higher-risk profiles. Professional help-seeking intentions were associated with reduced self-harm risk among distressed adolescents. CONCLUSIONS: Family and parental factors significantly shape adolescent coping profiles and mental health risks. Findings underscore the value of early screening and family-focused interventions to reduce self-harm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".