Key challenges to voluntary medical male circumcision uptake in traditionally circumcising settings of Machinga district in Malawi
Bibliographic record
Abstract
BACKGROUND: Voluntary medical male circumcision (VMMC) is becoming more popular as an important HIV prevention strategy. Malawi, with a high HIV and AIDS prevalence rate of 8.8% and a low male circumcision prevalence rate of 28% in 2016, is one of the priority countries recommended for VMMC scale-up. This paper investigates the attitudes and key challenges to VMMC adoption in a traditionally circumcising community in Malawi where male circumcision is culturally significant. METHODS: A mixed design study using quantitative and qualitative data collection methods was carried out to determine the attitudes of 262 randomly selected males towards VMMC in a culturally circumcising community in Malawi. Statistical Package for the Social Sciences (SPSS) version 20 was used to analyse the quantitative data. To identify predictors of VMMC uptake, we used logistic regression analysis. To identify the themes, qualitative data were analysed using content analysis. RESULTS: The findings indicate that, while more males in this community prefer medical circumcision, traditional circumcision is still practised. Panic (63%) perceived surgical complications (31%), and cost (27%) in accessing VMMC services were some of the barriers to VMMC uptake. Age and culture were found to be statistically significant predictors of voluntary medical male circumcision in the logistic analysis. According to qualitative data analysis, the key challenges to VMMC uptake were the involvement of female health workers in the circumcision team and the incentives provided to traditional circumcisers. CONCLUSION: According to the findings of this study, VMMC services should be provided in a culturally competent manner that respects and considers existing cultural beliefs and practices in the community. Coordination between local leaders and health workers should be encouraged so that VMMC services are provided in traditional settings, allowing for safe outcomes, and increasing VMMC uptake.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".