Determinant of Mental Emotional Disorder in Adolescent: A Cross-Sectional Study
Bibliographic record
Abstract
Objective: Adolescence is a critical period for experiencing mental disorders because this age is a transition from adolescence to adulthood, this period is also a determinant of one's mental health in the future. According to survey data from the Indonesia National Adolescent Mental Health Survey (I-NAMHS), which examined the prevalence of adolescent mental disorders in the country reveal that 1 out of 20 (5.5%) adolescents aged 10-17 years in Indonesia were diagnosed with a mental disorder. This study aimed to investigate the degree of risk of poor self-concept clarity, low self-esteem, a lack of peer support, and victims of bullying on mental-emotional disorders incidence in adolescents. Methods: This research is an observational study using a cross-sectional design. The sample in this study were high school students aged 15-18 years (n = 390) who were randomly selected from four high schools in Kotamobagu City, North Sulawesi Province, Indonesia. The data in this study were analyzed through the Chi-Square test and multiple regression test using the SPSS version 22.0. Results: This research shows that poor self-concept clarity (adj OR = 5.760; 95%CI = 3.173-10.458; p<0.001), low self-esteem (adj OR =3.647; 95%CI = 1.950-6.818; p<0.001), and victims of bullying (adj OR = 4.204; 95% CI =1.525-11.589; p=0.006) are related to adolescents’ mental-emotional disorders. Conclusions: It was concluded that the factors that influence mental-emotional disorders in adolescents are poor self-concept clarity, low self-esteem, and being a victim of bullying.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".