Predictors of Female Genital Mutilation or Cutting Among Daughters of Women in Guinea, West Africa
Bibliographic record
Abstract
Background and Objective: In some African countries like Guinea, female genital mutilation/cutting (FGM/C) has been considered as an essential social norm in ensuring girls’ and women’s virginity by reducing their sexual desires. This study aimed at examining the factors associated with FGM/C among daughters of women aged 15-49 in Guinea. Methods: Using the 2018 Guinea Demographic and Health Survey, we analyzed data on 10,721 women of reproductive age (15-49 years) who had at least one daughter. A two-level multi-level logistic regression analysis was fitted and the random and fixed effects together with their corresponding 95% credible intervals (95% CrIs) were presented. Results: Women of all age categories had higher odds of having circumcised daughters with the substantially highest odds among those aged 35-39 (aOR=26.10, CrI=11.22-53.94) compared to those aged 15-19. “FGM/C was higher among daughters of circumcised mothers (aOR=5.50, CrI=3.11-9.72), compared to those who were not circumcise. Compared to Muslims, women who were either animists or had no religion were more likely to circumcise their daughters (aOR=2.13, CrI=1.12-4.05). Conversely, women with secondary/higher education, whose partners had secondary/higher education, Christians, women of richest wealth index and those who lived in the Faranah and N’zerekore regions were less likely to circumcise their daughters. Conclusion and Implications for Translation: The current study revealed that individual and contextual factors are associated with FGM/C among daughters of women aged 15-49 in Guinea. The findings imply that eliminating FGM/C in Guinea requires multifaceted interventions such as advocacy and educational strategies like focus group discussions, peer teaching, mentor-mentee programs in regions noted with the FGM/C practice. This will help achieve the Sustainable Development Goal 5.3 which focuses on eliminating all harmful practices, such as child, early and forced marriage and female genital mutilation by 2030. Copyright © 2021 Ahinkorah. et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".