The relationship between insecurity and the quality of hospital care provided to women with abortion‐related complications in the Democratic Republic of Congo: A cross‐sectional analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between insecurity and quality of care provided for abortion complications in high-volume hospitals in the Democratic Republic of Congo (DRC). METHODS: Using the WHO Multi-Country Survey on Abortion complications, we analyzed data for 1007 women who received care in 24 facilities in DRC. For inputs of care, we calculated the percentage of facilities in secure and insecure areas meeting 12 readiness criteria for infrastructure and capability. For process and outcomes of care, we estimated the association between security and eight indicators using generalized estimating equation models. RESULTS: Facilities in secure areas were more likely to report functioning electricity (93.3% vs 66.7%), availability of an obstetrician 24/7 (42.9% vs 28.6%), and the ability to offer several short-acting contraceptives (83.3% vs 57.1%). However, a higher percentage of facilities in insecure areas reported the availability of a telephone or radio (100% vs 80.0%). Women in insecure areas appeared more likely to experience poor quality clinical care overall than women in secure areas (aOR 2.56; 95% CI, 1.13-5.82, P = 0.03). However, there was no association between security and incomplete medical records (P = 0.20), use of dilatation and curettage (D&C) (P = 0.84), women reporting poor experience of care (P = 0.22), satisfaction with care (P = 0.25), and severe maternal outcomes (P = 0.56). There was weak evidence of an association between security and nonreceipt of contraceptives (P = 0.07), with women in insecure areas 70% less likely to report no contraception (aOR 0.31, 95% CI, 0.09-1.09). Use of D&C was high in secure (43.7%) and insecure (60.4%) areas. CONCLUSION: Quality of care did not seem to be very different in secure and insecure areas in DRC, except for some key infrastructure, supply, and human resources elements. The frequent use of D&C for uterine evacuation, the lack of good record keeping, and the lack of contraceptives should be urgently addressed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".