Access of choice-disabled young women in Botswana to government structural support programmes: a cross-sectional study
Bibliographic record
Abstract
Structural factors like poverty, poor education, gender inequality, and gender violence are important in the HIV epidemic in southern Africa. Such factors constrain many people from making choices to protect themselves against HIV. The INSTRUCT cluster randomised controlled trial of a structural intervention for HIV prevention includes workshops for young women which link them with existing government structural support programmes. Fieldworkers identified all young women aged 15-29 years in each intervention community, not in school and not in work, interviewed them, and invited them to a workshop. Choice-disability factors were common. Among the 3516 young women, 64% had not completed secondary education, 35% did not have enough food in the last week, 21% with a partner had been beaten by their partner in the last year, and 8% reported being forced to have sex. Of those aged 18 and above, 45% had applied to any government support programme and 28% had been accepted into a programme; these rates were only 33% and 10% when Ipelegeng, a part-time minimum wage rotating employment scheme with no training or development elements, was excluded. Multivariate analysis considering all programmes showed that women over 20 and very poor women with less education were more likely to apply and to be accepted. But excluding Ipelegeng, young women with more education were more likely to be accepted into programmes. The government structural support programmes were not designed to benefit young women or to prevent HIV. Our findings confirm that programme use by marginalised young women is low and, excluding Ipelegeng, the programmes do not target choice disabled young women.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".