Association of ADHD symptoms, depression and suicidal behaviors with anxiety in Chinese medical college students
Bibliographic record
Abstract
BACKGROUND: Anxiety is one of the most common psychiatric disorder and imposes a great burden on both the individual and the society. Previous studies indicate a high comorbidity of anxiety disorders and Attention Deficit Hyperactivity Disorder (ADHD). However, few studies have examined the comorbidity of anxiety and ADHD among medical college students in mainland China. This study aimed to examine the prevalence of anxiety and the associated risk factor of anxiety disorder as well as to explore the association between ADHD symptoms, depression, suicidal behaviors and anxiety. METHODS: A cross-sectional design was employed among 4882 medical college students who were recruited and enrolled with convenience sampling. Self-reported demographic information and clinical characteristics were collected online on a computer or through a social media app named Wechat. RESULTS: The prevalence of anxiety in this study was 19.9%. Students with anxiety were more likely to have a poor relationship with parents, be of Han nationality, have smoking or drinking habits, have an extensive physical disorder history and have engaged in suicidal behaviors. The independent risk factors for anxiety were: smoking, physical disorder history, suicidal ideations, suicide attempts, inattention and hyperactivity. Significant associations were observed between anxiety and depression, inattention, hyperactivity, suicide plans and suicide attempts. CONCLUSIONS: Nearly one in five medical students suffered from anxiety. The findings of this study indicate the importance of addressing both anxiety and ADHD symptoms in order to better promote mental health and the well-being of medical students as well as reduce suicidal behaviors.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".