Gender-based violence in the context of armed conflict in Northern Ethiopia
Bibliographic record
Abstract
BACKGROUND: Gender-based violence (GBV) particularly against women is unfortunately common during armed conflicts. No rigorous and comprehensive empirical work has documented the extent of GBV and its consequences that took place during the two years of devastating armed conflict in Northern Ethiopia. This study aims to assess GBV and its consequences in war-torn areas of northern Ethiopia. METHODS: We used a qualitative method augmented by quantitative method to enroll research participants. We conducted in-depth interviews to characterize the lived experiences of GBV survivors. All interviews were conducted confidentially. The data were collected to the point of data saturation. All interviews were transcribed verbatim into local language, translated into English, and analyzed using a thematic analysis approach. We also used reports from healthcare facilities and conducted a descriptive analysis of the demographic characteristics of study participants. RESULTS: One thousand one hundred seventy-seven persons reported GBV to healthcare providers. The qualitative study identified several forms of violence (sexual, physical, and psychological). Gang rape against women including minors as young as 14 years old girls was reported. Additionally, the perpetrators sexually violated women who were pregnant, and elderly women as old as 65 years, who took refuge in religious institutions. The perpetrators committed direct assaults on the body with items (e.g., burning the body with cigarette fire) or weapons, holding women and girls as captives, and deprivation of sleep and food. GBV survivors reported stigma, prejudice, suicide attempts, nightmares, and hopelessness. GBV survivors dealt with the traumatic stress by outmigration (leaving their residences), seeking care at healthcare facilities, self-isolation, being silent, dropping out of school, and seeking counseling. CONCLUSION: GBV survivors were subjected to multiple and compounding types of violence, with a wide range of adverse health consequences for survivors and their families. GBV survivors require multifaceted interventions including psychological, health, and economic support to rehabilitate them to lead a productive life.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".