Mental Health Problems and Associated Factors in Chinese High School Students in Henan Province: A Cross-Sectional Study
Bibliographic record
Abstract
Approximately one in five adolescents experience mental health problems globally. However, studies on mental health problems in Chinese high school students are few. Therefore, this study examined the status and associated factors of mental health problems in high school students in China. A stratified two-stage cluster sampling procedure was adopted, leading to a final sample of 15,055 participants from 46 high schools in all 17 provincial cities of Henan province, China. Self-reported questionnaires were used to collect the data. A mental health problems variable was assessed using the Mental Health Inventory of Middle School Students. The positive rate of mental health problems among high school students was 41.8%, with a male predominance (43.3% versus 40.2% in females; p < 0.01). The most frequent mental health problem was academic stress (58.9%). Higher grades, physical disease, chronic constipation, alcohol consumption, engagement in sexual behavior, residence on campus, and living in nonurban areas and with single-parent families were significantly associated with higher odds of having mental health problems (p < 0.05). We suggest that the prevention of mental health problems in high school students be strengthened, especially in students with physical illnesses, unhealthy behaviors, and single-parent families.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".