Mixed results: the protective role of schooling in the HIV epidemic in Swaziland
Bibliographic record
Abstract
Swaziland has the highest HIV prevalence in the world. It is recognised that young women, especially adolescents, are particularly vulnerable to HIV infection and bear a disproportionate burden of HIV incidence. The HIV data from Swaziland show the location of the epidemic, which is particularly high among adolescent girls and young women. This paper is based on research in Swaziland, commissioned because of the perception that large numbers of children were dropping out of the school. It was assumed that these "dropouts" had increased risk of HIV exposure. This study carried out a detailed analysis using the Annual Education Census Reports from 2012 to 2014 produced by the Ministry of Education. In addition, this topic was explored, during fieldwork with key informants in the country. While HIV prevalence rises rapidly among young women in Swaziland, as is the case across most of Southern Africa, the data showed there were few dropouts. This was the case at all levels of education - primary, junior secondary and senior secondary. The major reason for dropping out of primary school was family reasons; and in junior and senior secondary, pregnancy was the leading cause. Swaziland is doing well in terms of getting its children into school, and, for the most part, keeping them there. This paper identifies the students who face increased vulnerability: the limited number of dropouts; repeaters who consequently were "out-of-age for grade"; and orphans and vulnerable children (OVC). The learners who were classified as repeaters and OVC greatly outnumbered the dropouts. We argue, on the basis of these data, for re-focussed attention and the need to develop a method for tracking children as they move across the vulnerable groups. We acknowledge schooling is protective in reducing children's vulnerability to HIV, and Swaziland is on the right track in education, although there are challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".