“I became a person again”: Social inclusion and participation experiences of Ethiopian women post-obstetric fistula surgical repair
Bibliographic record
Abstract
BACKGROUND: Childbirth-related mortality and morbidity affect many women globally, especially in low-income countries like Ethiopia. Obstetric fistula-a preventable condition mainly caused by prolonged and obstructed labor-can lead to physical, psychological, and social challenges, affecting women's social participation and inclusion. OBJECTIVE: This study aims to understand women's social participation and inclusion experiences post-obstetric fistula surgery. METHODS: This study is part of a larger research project investigating the social inclusion process of women who have had obstetric fistula surgery in Ethiopia. For this study, we conducted a qualitative exploration of women's experiences, guided by a constructivist grounded theory approach. Twenty-one women discharged from fistula treatment facilities following obstetric fistula surgery were interviewed using a semi-structured interview guide. Data was analyzed using Charmaz's inductive analysis approach, which involves an initial line-by-line coding followed by focused coding to identify the most significant codes. Subsequently, sub-themes and themes were developed from the focused codes. RESULT: The data analysis revealed four themes reflecting the women's experiences of social participation and inclusion. These are the experience of recovery and the journey toward social participation, participating in expected and meaningful activities, the continued challenge with a romantic relationship, and formal and informal support. Overall, the women who received fistula surgery reported positive life changes, especially regarding their physical well-being. However, they continued to face social challenges such as financial hardship, reproductive health problems, and issues with marriage and family life, which negatively impacted their social participation and inclusion experiences. CONCLUSION: While more research is needed, the findings of this study suggest that the social aspects of obstetric fistula are crucial for healthcare professionals to consider. Providing appropriate care and support to address unmet social relationship, employment, and childcare needs could enable women to lead fulfilling lives.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".