Women’s empowerment and female genital mutilation intention for daughters in Sierra Leone: a multilevel analysis
Bibliographic record
Abstract
BACKGROUND: Female genital mutilation is common in Sierra Leone. Evidence indicates that empowering women provides protective benefits against female genital mutilation/cutting (FGM/C). Yet, the relationship between women's empowerment and their intention to cut their daughters has not been explored in Sierra Leone. The aim of this study was to assess the association between women's empowerment and their intention to have their daughters undergo FGM/C in the country. METHODS: Data for this study are from the 2013 Sierra Leone Demographic and Health Survey. A total of 7,706 women between the ages of 15 and 49 were included in the analysis. Analysis entailed generation of descriptive statistics (frequencies and percentages), and estimation of multi-level logistic regression models to examine the association between women's empowerment, contextual factors and their intentions to cut their daughters. RESULTS: A significantly higher proportion of women who participated in labour force reported that they intended to cut their daughters compared to those who did not (91.2%, CI = 90.4-91.9 and 86.0%, CI = 84.1-87.8, respectively). Similarly, the proportion intending to cut their daughters was significantly higher among women who accepted wife beating than among those who rejected the practice (94.9%, CI = 93.8-95.8 and 86.4% CI = 84.9-87.8, respectively). A significantly higher proportion of women with low decision-making power intended to cut their daughters compared to those with high decision-making power (91.0%, CI = 89.0-92.8 and 85.0% CI = 82.2-87.4, respectively). Results from multivariate regression analysis showed that the odds of intending to cut daughters were significantly higher among women who participated in labour force (aOR = 2.5, CI = 1.3-4.7) and those who accepted wife beating than among those who did not (aOR = 2.7, CI = 1.7-4.5). In contrast, the likelihood of intending to cut daughters was significantly lower among women with high than low knowledge (aOR = 0.4, CI = 0.3-0.7), and among those aged 45-49 than among those aged 15-19 (aOR = 0.2, CI = 0.0-0.6). CONCLUSION: The findings underscore the need to align anti-FGM/C policies and programmes to women who have undergone FGM/C, those with low knowledge, women who support wife beating and young women. Such interventions could highlight the adverse implications of the practice by stressing the psychological, health and social implications of FGM/C on its survivors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".