Gender inequalities in the prevalence of low mood and related factors in schooled adolescents during the 2019–2020 school year: DESKcohort project
Bibliographic record
Abstract
BACKGROUND: Mood disorders are the second most prevalent mental disorders in childhood and adolescence. Many undiagnosed people manifest subthreshold symptoms, like low mood, and present worse prognoses than asymptomatic healthy subjects. The aim of this study was to estimate the prevalence of low mood, gender inequalities, and associated factors, in 12- to 18-year-old adolescents in the rural and medium-sized urban areas of Central Catalonia during the 2019-2020 academic year. METHODS: Cross-sectional study with data from a cohort of high-schooled students (2019-2020), with a convenience sample of 6428 adolescents from the Central region of Catalonia (48.3 % boys and 51.7 % girls). Prevalence of low mood was estimated by gender and exposure variables, and ratios were obtained using Poisson regression models, adjusting for several exposure variables one by one, and for all of them jointly. RESULTS: The prevalence of low mood was 18.6 %, with statistically significant differences between genders (11.6 %, 95 % CI: 10.5-12.8 in boys and 25.1 %, 95 % CI: 23.7-26.6 in girls). Being an immigrant, dieting, and daily tobacco smoking were only associated with low mood in girls, whereas risky alcohol consumption was only associated in boys. Sexual violence was found to account for 36.2 % of low mood problems in girls. LIMITATIONS: The main limitation of the study is its cross-sectional design, which means that no casual relationships can be extracted of this study. CONCLUSIONS: The prevalence of low mood varies between the sexes, highlighting the importance of developing gender-specific interventions to reduce its incidence in young people, considering the factors associated with this condition.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".