Predictors of mental health problems in adolescents living with <scp>HIV</scp> in Namibia
Bibliographic record
Abstract
BACKGROUND: Little research in sub-Saharan Africa has looked at factors that predict mental health problems in adolescents living with HIV (ALHIV). This study examines the psychological impact of HIV in adolescents in Namibia, including risk and protective factors associated with mental health. METHODS: Ninety-nine fully disclosed ALHIV between the ages of 12 and 18 were interviewed at a State Hospital in Windhoek. A structured questionnaire assessed mental health, using the SDQ (Goodman, 1997), sociodemographic factors, poverty, social support, adherence and stigma. RESULTS: Mean age was 14.3 years, 52.5% were female and most were healthy. Twelve percent scored in the clinical range for total mental health difficulties and 22% for emotional symptoms. Poverty was associated with more total mental health difficulties, t(96) = -2.63, p = .010, and more emotional symptoms, t(96) = -3.45, p = .001, whereas better social support was a protective factor, particularly caregiver support (r = -.337, p = .001). Adherence problems, HIV-related stigma and disclosing one's own HIV status to others were also associated with more total mental health difficulties. Poverty (β = -.231, p = .023) and stigma (β = .268, p = .009) were the best predictors for total mental health difficulties, whereas stigma (β = .314, p = .002) predicted emotional symptoms. Social support had a protective effect on peer problems (p = .001, β = -.349). CONCLUSIONS: Several contextual factors associated with poorer mental health in ALHIV are identified.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".