Understanding the motivations of health-care providers in performing female genital mutilation: an integrative review of the literature
Bibliographic record
Abstract
BACKGROUND: Female genital mutilation (FGM) is a traditional harmful practice that can cause severe physical and psychological damages to girls and women. Increasingly, trained health-care providers carry out the practice at the request of families. It is important to understand the motivations of providers in order to reduce the medicalization of FGM. This integrative review identifies, appraises and summarizes qualitative and quantitative literature exploring the factors that are associated with the medicalization of FGM and/or re-infibulation. METHODS: Literature searches were conducted in PubMed, CINAHL and grey literature databases. Hand searches of identified studies were also examined. The "CASP Qualitative Research Checklist" and the "STROBE Statement" were used to assess the methodological quality of the qualitative and quantitative studies respectively. A total of 354 articles were reviewed for inclusion. RESULTS: Fourteen (14) studies, conducted in countries where FGM is largely practiced as well as in countries hosting migrants from these regions, were included. The main findings about the motivations of health-care providers to practice FGM were: (1) the belief that performing FGM would be less harmful for girls or women than the procedure being performed by a traditional practitioner (the so-called "harm reduction" perspective); (2) the belief that the practice was justified for cultural reasons; (3) the financial gains of performing the procedure; (4) responding to requests of the community or feeling pressured by the community to perform FGM. The main reasons given by health-care providers for not performing FGM were that they (1) are concerned about the risks that FGM can cause for girls' and women's health; (2) are preoccupied by the legal sanctions that might result from performing FGM; and (3) consider FGM to be a "bad practice". CONCLUSION: The findings of this review can inform public health program planners, policy makers and researchers to adapt or create strategies to end medicalization of FGM in countries with high prevalence of this practice, as well as in countries hosting immigrants from these regions. Given the methodological limitations in the included studies, it is clear that more robust in-depth qualitative studies are needed, in order to better tackle the complexity of this phenomenon and contribute to eradicating FGM throughout the world.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".