Fertility desires and infection with the HIV: results from a survey in rural Uganda
Bibliographic record
Abstract
OBJECTIVE: To determine how a positive HIV diagnosis influences fertility desires and reproductive decisions for women and men living in western Uganda. DESIGN: : Cross-sectional survey comparing two groups of participants: those who tested HIV-positive and those who tested HIV-negative. METHODS: Semistructured interviews were conducted with 421 individuals living in rural areas. Descriptive, bivariate and multivariate methods were used to analyze the relationship of the HIV status to the desire to stop childbearing, reproductive decision-making and to the use of family planning methods. RESULTS: The multivariate logistic regression model indicated that the odds ratio (OR) of wanting to stop childbearing was found to be 6.25 times greater (P < 0.01) for HIV-positive than for HIV-negative individuals. Additional predictors included older age (OR 1.13, P < 0.01), female sex (OR 2.42, P = 0.03), Mutooro ethnic group (OR 3.20, P < 0.01) and greater number of living children (OR 1.62, P < 0.01). Use of dual protection against HIV/sexually transmitted infection and unwanted pregnancy was rare in both groups with seven HIV-positive participants (3.5%) using two contraceptives compared with only one (0.4%) in the HIV-negative group. The unmet need for a highly effective family planning method was higher in HIV-positive participants compared with HIV-negative ones (90 vs. 78%). CONCLUSION: HIV-positive individuals in the Kabarole region have a much greater desire to stop childbearing than their HIV-negative counterparts. The barriers to utilizing family planning services, as evidenced through the very low use of highly effective contraceptive methods, have to be jointly addressed by HIV/AIDS care/prevention and family planning programs.
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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 | 0.003 |
| 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 | 0.000 | 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".