Mental health in HIV-positive pregnant women: Results from Angola
Bibliographic record
Abstract
Our objective was to assess the mental health status of pregnant women who are HIV-positive, compared with other groups of pregnant women. We evaluated pregnant HIV-positive women attending the Medecins Sans Frontiers (MSF) HIV clinic in Malanje, Angola (N = 23). The control group consisted of pregnant women coming for antenatal clinic consultations who were not known to be HIV-positive (N=134). To assess mental health, the 12-item General Health Questionnaire (GHQ-12) was used. A score of three or greater was considered to indicate significant emotional distress. We also examined determinants of emotional distress in logistic multivariate regression models. We found that the mean score on the GHQ-12 for the HIV-positive group was more than twice the mean score of the controls, indicating poorer mental health in the HIV-positive group. Two-thirds of HIV-positive women had significant emotional distress, more than twice that in the control group. As well as HIV status, marital status was a strong independent predictor of mental health status, with married women experiencing less emotional distress. Thus, in our sample, pregnant women who were HIV-positive had a much poorer mental health status than the controls. Strategies to improve the mental health of HIV-positive mothers must be implemented and evaluated; efforts to decrease the levels of stigma and discrimination in this population are of key importance.
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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 | 0.000 |
| 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 | 0.000 |
| 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".