Prevalence and patterns of gender-based violence among adolescent girls fetching water in Peri-Urban Settings of Kinshasa, DR Congo
Bibliographic record
Abstract
ABSTRACT In water-scarcity contexts, girls fetching water are exposed to gender-based violence (GBV), for which prevalence, types, and forms were unknown in the Peri-Urban Settings of Kinshasa. A cross-sectional study using multi-stage random sampling technique to select 684 adolescent girls was conducted to assess the extent of water scarcity and GBV affecting adolescent girls while fetching water. Findings indicate that 98.2% of adolescent girls were dealing with water shortage; 99.9% experienced at least one type of GBV, of which 97.1, 95.5, and 44.9% experienced sexual, psychological, and physical violence, respectively. Moral violence was more frequent at water points; physical violence in the household, while sexual violence was prevalent on the water route. Adolescent girls' age, weekly involvement in water collection, and distance were found to be the main factors associated with GBV, whereas reducing the number of daily round-trips, the distance travelled, and time devoted to water collection were found to be mitigating factors limiting GBV experience among adolescent girls. Policies promoting the at-home provision of water and community awareness-raising interventions will mitigate the GBV incidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".