Social determinants of mental health among older adolescent girls living in urban informal settlements in Kenya and Nigeria during the COVID-19 pandemic
Bibliographic record
Abstract
The health burden due to mental health has historically been underestimated with focus on communicable diseases and deaths and little consideration of disability and comorbidity effects of poor mental health. Recent data show increasing trends of mental health disorders as a share of global health burdens and vulnerability of adolescents. This paper aims to explore social determinants of mental health as experienced by adolescent girls, drawing attention to gendered risks during the COVID-19 pandemic. Semi-structured interviews with twenty-two adolescent girls in urban informal settlements in Kenya and Nigeria reveal unique environmental, socio-cultural, economic and educational factors that threatened their mental wellbeing. The pandemic exacerbated these determinants. An equitable recovery will require a consideration of not only disproportional mental health outcomes, but also social determinants that contribute to these outcomes. As more than half of the urban population in sub-Saharan Africa reside in informal settlements, this study has implications for youth-focused mental health interventions in these and similar settings.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".