The Sexual and Reproductive Health Context of an Internally Displaced Persons' Camp in Northeastern Nigeria: Narratives of Girls and Young Women
Bibliographic record
Abstract
In humanitarian settings, ~35 million girls and young women of reproductive age (15–24) are in urgent need of sexual and reproductive health (SRH) information and services. Young women and girls in humanitarian contexts are particularly vulnerable to unwanted pregnancies, unsafe abortion, gender-based violence, and early and forced marriage. We sought to understand girls' and young women's experiences with unwanted pregnancy, abortion, contraception, sexually transmitted infections (STIs), gender-based violence (GBV), and forced marriage in an IDP camp in Northeastern Nigeria. We conducted 25 in-depth interviews with girls aged 15–19 ( N = 13; 8 single and 5 married) and young women aged 20–24 ( N = 12; 3 single and 9 married). All interviews were audiotaped, transcribed, translated, computer recorded and coded for analysis. The participants in our study fled from and witnessed violence to arrive in the IDP camp with little material support. Lack of necessities, especially food, has driven many to sex in exchange for goods or into forced marriages. This, in turn, leads to increased unwanted pregnancies and unsafe abortions. Participants had limited knowledge about contraception, and some information about SRH services available in the camp, but overall, knowledge and utilization of SRH services was low.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".