The Role of Female Partners in the uptake of Voluntary Medical Male Circumcision in Sub-Saharan Africa: A Review
Bibliographic record
Abstract
BACKGROUND: Voluntary Medical Male Circumcision (VMMC) is a proven biological strategy for reducing heterosexual transmission of HIV/AIDS by up to 60%. Following recommendations from the World Health Organisation (WHO), Medical Circumcision (MC) was rolled out in South Africa. Several issues, among them being individual perceptual factors and female partner influence, have constituted as both obstacles and drivers to the uptake of VMMC. AIM: To explore and synthesize research conducted on the role of female partners in the uptake of VMMC. METHODS: Electronic searches were conducted in PUBMED, MEDLINE and CIHNAL, studies included in the review are those that explored the importance of female partner involvement in the uptake of VMMC. The review was limited to sub-Saharan Africa with a focus on peer reviewed articles written in English only. RESULTS: The review has revealed that considering the gender dimensions of circumcision, the possible utilisation of women as vehicles to drive the uptake of MC could be key to achieving the desired uptake. CONCLUSION: It is postulated that women play a key role in terms of promoting circumcision in order to facilitate a successful scale up of the service. Further research is therefore necessary so that the benefits of female partner involvement in VMMC may be achieved.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".