Prevalence and factors associated with depression among higher secondary school adolescents of Pokhara Metropolitan, Nepal: a cross-sectional study
Bibliographic record
Abstract
Objective This study examined the prevalence and factors associated with depression among adolescents attending higher secondary schools in the Pokhara Metropolitan City of Nepal.Design A cross-sectional study design was adopted.Setting Four randomly selected higher secondary schools of Pokhara Metropolitan, Nepal.Participants 312 randomly sampled higher secondary school students.Methods The Center for Epidemiologic Studies Depression Scale was used to assess the level of depression among students. The data collected through a self-administered questionnaire were analysed using descriptive statistical methods such as frequency and percentage. χ2 test and unadjusted OR (UOR) were calculated to assess the statistical relationship between depression and various variables at 95% CI, with level of significance at p<0.05.Results The study found a high prevalence of depression among high school students, with more than two-fifths (44.2%) of students having depression. Furthermore, almost a quarter (25.3%) of the students were noted to have mild depression and 18.9% of the students expressed major depression. Students who had low perceived social support (UOR: 3.604; 95% CI 2.088 to 6.220), did not share their problems with anyone (UOR: 1.931; 95% CI 1.228 to 3.038) and had low self-esteem (UOR: 5.282; 95% CI 2.994 to 9.319) were at higher odds of being depressed.Conclusion A high prevalence of depression was observed among high school students. It was also observed that students’ level of perceived social support, self-esteem and help-seeking behaviour are somehow related to their mental well-being. Hence, improving social support and self-esteem may alleviate depression and mental distress among these adolescents.
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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 | 0.000 | 0.001 |
| 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".