The impact of care environment on the mental health of orphaned, separated and street-connected children and adolescents in western Kenya: a prospective cohort analysis
Bibliographic record
Abstract
INTRODUCTION: The effect of care environment on orphaned and separated children and adolescents' (OSCA) mental health is not well characterised in sub-Saharan Africa. We compared the risk of incident post-traumatic stress disorder (PTSD), depression, anxiety and suicidality among OSCA living in Charitable Children's Institutions (CCIs), family-based care (FBC) and street-connected children and youth (SCY). METHODS: This prospective cohort followed up OSCA from 300 randomly selected households (FBC), 19 CCIs and 100 SCY in western Kenya from 2009 to 2019. Annual data were collected through standardised assessments. We fit survival regression models to investigate the association between care environment and mental health diagnoses. RESULTS: The analysis included 1931 participants: 1069 in FBC, 783 in CCIs and 79 SCY. At baseline, 1004 participants (52%) were male with a mean age (SD) of 13 years (2.37); 54% were double orphans. In adjusted analysis (adjusted HR, AHR), OSCA in CCIs were significantly less likely to be diagnosed with PTSD (AHR 0.69, 95% CI 0.49 to 0.97), depression (AHR 0.48 95% CI 0.24 to 0.97), anxiety (AHR 0.56, 95% CI 0.45 to 0.68) and suicidality (AHR 0.73, 95% CI 0.56 to 0.95) compared with those in FBC. SCY were significantly more likely to be diagnosed with PTSD (AHR 4.52, 95% CI 4.10 to 4.97), depression (AHR 4.72, 95% CI 3.12 to 7.15), anxiety (AHR 4.71, 95% CI 1.56 to 14.26) and suicidality (AHR 3.10, 95% CI 2.14 to 4.48) compared with those in FBC. CONCLUSION: OSCA living in CCIs in this setting were significantly less likely to have incident mental illness, while SCY were significantly more, compared with OSCA in FBC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".