Roads of restoration and resilience: A journey into clitoral restoration surgery in Canada
Bibliographic record
Abstract
,Female genital cutting (FGC) has a profound impact on the physical, mental, and sexual health of survivors - leading to various long-term health complications overtime. Clitoral restoration surgery (CRS) has emerged as a potential solution to these complications through the surgery’s acclaimed ability to alleviate pain, restore sexual function, and improve survivors’ sense of self. However, these claims have been challenged, highlighting insufficient evidence and lack of consideration for the psychosexual and cultural contexts that influence survivors’ surgical outcomes. Therefore, understanding FGC as an ongoing experience rather than a onetime event emphasizes the complexity of its effects on survivors. In order to account for this complexity, this study employs a combination of theoretical perspectives, including complexity theory, intersectional feminist theory, and multisystemic resilience. These frameworks help to investigate questions of Canadian survivors’ motivations in electing to undergo CRS, the specific complications they must present with in order to qualify for this surgical procedure, and physicians’ decision-making when it comes to identifying potential candidates. Through survivors, care providers, and FGC-related organizations, this study explores these questions. By examining the decision-making process between physician and their patient, this study emphasizes the importance of individualized care that accounts for survivors’ unique physical, social, and cultural contexts. Additionally, this research underscores the importance of more holistic treatment options, such as psychosexual therapy and sexual education, as potential alternatives to CRS. These approaches may help to address survivors’ health concerns without the need for surgical intervention. By promoting awareness of these alternatives, the study encourages a more comprehensive understanding of healing that moves beyond physical health to also emphasize survivors’ mental and emotional well-being along their healing journeys. Lastly, by contributing to the limited body of knowledge on CRS and FGC-related care more broadly in Canada, this research helps to raise awareness of the challenges faced by survivors, combat stigma in health-care settings, and support the development of relevant health policies. Insights gained from survivors, care providers, and FGC-related organizations in this study highlight the interconnectedness of these groups - fostering collaborative efforts to improve survivor care and support services in both Canada and abroad.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".