Barriers to obstetric fistula treatment in low‐income countries: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To identify the barriers faced by women living with obstetric fistula in low-income countries that prevent them from seeking care, reaching medical centres and receiving appropriate care. METHODS: Bibliographic databases, grey literature, journals, and network and organisation websites were searched in English and French from June to July 2014 and again from August to November 2016 using key search terms and specific inclusion and exclusion criteria for discussion of barriers to fistula treatment. Experts provided recommendations for additional sources. RESULTS: Of 5829 articles screened, 139 were included in the review. Nine groups of barriers to treatment were identified: psychosocial, cultural, awareness, social, financial, transportation, facility shortages, quality of care and political leadership. Interventions to address barriers primarily focused on awareness, facility shortages, transportation, financial and social barriers. At present, outcome data, though promising, are sparse and the success of interventions in providing long-term alleviation of barriers is unclear. CONCLUSION: Results from the review indicate that there are many barriers to fistula treatment, which operate at the individual, community and national levels. The successful treatment of obstetric fistula may thus require targeting several barriers, including depression, stigma and shame, lack of community-based referral mechanisms, financial cost of the procedure, transportation difficulties, gender power imbalances, the availability of facilities that offer fistula repair, community reintegration and the competing priorities of political leadership.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".