Is female genital mutilation/cutting associated with adverse mental health consequences? A systematic review of the evidence
Bibliographic record
Abstract
INTRODUCTION: The adverse physical consequences of female genital mutilation/cutting (FGM/C) have been thoroughly investigated and documented. Yet, we know little about the adverse mental health consequences of the practice. To fill this research gap, we systematically reviewed studies that assessed any adverse mental health consequences related to FGM/C. METHODS: We searched four databases from inception to 21 December 2018. We then reviewed all titles and abstracts for relevant studies. We used the National Institutes of Health quality assessment tool to appraise the quality of each study and the Newcastle-Ottawa Scale to rate the risk of bias within studies. RESULTS: We included 16 studies in this review; only six studies examined the association between FGM/C and adverse mental health outcomes as the sole research question. Among the included studies, 10 were conducted at the participants' country of origin. The sample size of the populations studied ranged from 3 to 4800 participants. Only one study received a rating of 'good' methodological quality.Fourteen of the 16 studies reported an association between FGM/C and at least adverse mental health outcome. These included eight studies that reported a higher burden of adverse mental health outcomes among women who underwent FGM compared with women who did not undergo FGM/C. Four studies reported a correlation between the severity of FGM/C and the severity of adverse mental health outcomes. CONCLUSION: This systematic review documents an association between FGM/C and adverse mental health outcomes. Importantly, our review demonstrates the need for more rigorous research on the topic.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".