Open Access Violence against civilians and access to health care in North Kivu, Democratic Republic of Congo: three cross-sectional surveys
Bibliographic record
Abstract
Background: The province of North Kivu in the Democratic Republic of Congo has been afflicted by conflict for over a decade. After months of relative calm, offences restarted in September 2008. We did an epidemiological study to document the impact of violence on the civilian population and orient pre-existing humanitarian aid. Methods: In May 2009, we conducted three cross-sectional surveys among 200 000 resident and displaced people in North Kivu (Kabizo, Masisi, Kitchanga). The recall period covered an eight month period from the beginning of the most recent offensives to the survey date. Heads of households provided information on displacement, death, violence, theft, and access to fields and health care. Results: Crude mortality rates (per 10 000 per day) were below emergency thresholds: Kabizo 0.2 (95 % CI: 0.1-0.4), Masisi 0.5 (0.4-0.6), Kitchanga 0.7 (0.6-0.9). Violence was the reported cause in 39.7 % (27/68) and 35.8 % (33/92) of deaths in Masisi and Kitchanga, respectively. In Masisi 99.1 % (897/905) and Kitchanga 50.4 % (509/1020) of households reported at least one member subjected to violence. Displacement was reported by 39.0 % of households (419/1075) in Kitchanga and 99.8 % (903/905) in Masisi. Theft affected 87.7 % (451/514) of households in Masisi and 57.4 % (585/1019) in Kitchanga. Access to health care was good: 93.5 % (359/384) of the sick in Kabizo, 81.7 % (515/630) in Masisi, and 89.8 % (651/725) in Kitchanga received care, of whom 83.0 % (298/359), 87.5 % (451/515), and 88.9 % (579/651),
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".