Status and risk factors for depression, anxiety, and insomnia symptoms among adolescents in the post-pandemic era
Bibliographic record
Abstract
BACKGROUND: Owing to their vulnerability to public health crises, adolescents' mental health status, and related risk factors in the post-pandemic era in China require examination. This study aimed to lay a foundation for subsequent interventions and mental disorder prevention among adolescents. METHODS: An anonymous online survey was conducted January 8-18, 2023. The survey comprised two parts. The first part collected personal information. The second part included the nine-item Patient Health Questionnaire, seven-item Generalized Anxiety Disorder Scale, and Pittsburgh Sleep Quality Index. RESULTS: Of 50,666 adolescent participants, 46.8% displayed depressive symptoms, 31.5% showed anxiety symptoms, and 47.9% experienced difficulties initiating sleep within 30 min, while 4.9% manifested depressive, anxiety, and insomnia symptoms simultaneously. Chi-square tests revealed significant variations in the prevalence of these conditions according to age, sex, coronavirus disease-2019 (COVID-19) infection status, family infection status, and increased screen time (P < 0.001). Logistic regression analysis indicated heightened rates of depression, anxiety, and insomnia symptoms among rural adolescents, females, those aged 16-18 years, individuals with a history of COVID-19, those with a long COVID-19 illness, and those with increased screen time (all P < 0.001). CONCLUSION: The mental health status of adolescents in the post-pandemic era should be taken seriously, particularly that of females, high school students, and adolescents with increased use of electronic screens. Among such efforts, school administrators should strengthen their connection with psychologists, as early identification and intervention in adolescents are important for preventing school crises.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".