DEPRESSION AMONG CHINESE CHILDREN AND ADOLESCENTS: A REVIEW OF THE LITERATURE
Bibliographic record
Abstract
<p><span style="font-family: Times New Roman; font-size: small;">The purpose of this review is to explore, identify, and discuss the predisposing factors and associated outcomes of depression in Chinese children and adolescents. <span style="font-family: Times New Roman; color: #262626;">For inclusion in the review, studies had to meet our objectives, be original peer-reviewed articles, conducted among Chinese children and adolescents in China. Articles were sourced through MEDLINE, EMBASE, </span></span><span style="font-family: Times New Roman; color: #262626; font-size: small;">Wan Fang Data</span><span style="font-family: Times New Roman; color: #262626; font-size: small;">,</span><span style="font-family: Times New Roman; color: #262626; font-size: small;"> </span><span style="font-family: Times New Roman; font-size: small;">PsycINFO,</span><span style="font-family: Times New Roman; color: #262626; font-size: small;"> and DOAJ databases. The results of the review indicate that the prevalence of depression symptoms in Chinese children and adolescents is high. The following factors are related to depression in Chinese children and adolescents: family, social factors, peer relations, gender, age, obesity, body image, and ethnicity. The outcomes of depression are poor academic performance, psychosocial retardation, conduct problems, cognitive distortion, and suicide. Depression is a major mental health problem among Chinese children and adolescents. This points to the need for longitudinally designed and controlled studies to establish effective preventive strategies.</span></p>
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".