Female genital mutilation/cutting among girls aged 0–14: evidence from the 2018 Mali Demographic and Health Survey data
Bibliographic record
Abstract
BACKGROUND: Female genital mutilation/cutting (FGM/C) is considered a social norm in many African societies, with varying prevalence among countries. Mali is one of the eight countries with very high prevalence of FGM/C in Africa. This study assessed the individual and contextual factors associated with female FGM/C among girls aged 0-14 years in Mali. METHODS: We obtained data from the 2018 Mali Demographic and Health Survey. The prevalence of FGM/C in girls was presented using percentages while a multilevel binary logistic regression analysis was conducted to assess the predictors of FGM/C and the results were presented using adjusted odds ratios with associated 95% confidence intervals (CIs). RESULTS: The results indicate that more than half (72.7%, 95% CI = 70.4-74.8) of women in Mali with daughters had at least one daughter who has gone through circumcision. The likelihood of circumcision of girls increased with age, with women aged 45-49 having the highest odds compared to those aged 15-19 (aOR = 17.68, CI = 7.91-31.79). A higher likelihood of FGM/C in daughters was observed among women who never read newspaper/magazine (aOR = 2.22, 95% CI = 1.27-3.89), compared to those who read newspaper/magazine at least once a week. Compared to women who are not circumcised, those who had been circumcised were more likely to have their daughters circumcised (aOR = 53.98, 95% CI = 24.91-117.00). CONCLUSION: The study revealed the age of mothers, frequency of reading newspaper/magazine, and circumcision status of mothers, as factors associated with circumcision of girls aged 0-14 in Mali. It is, therefore, imperative for existing interventions and new ones to focus on these factors in order to reduce FGM/C in Mali. This will help Mali to contribute to the global efforts of eliminating all harmful practices, such as child, early and forced marriage and female genital mutilation by 2030.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".