Reproductive Health for Conflict-Affected Displaced Women in Nigeria: An Intersectionality-Based Critical Ethnography Study
Bibliographic record
Abstract
Abstract Nigeria is a significant contributor to the global forcibly displaced population. The majority of this displacement is related to Boko Haram and herdsmen attacks in Northern Nigeria. A growing body of research has started to investigate issues surrounding protection concerns for the internally displaced who have been uprooted by these uprisings and attacks. Importantly, research is also starting to engage with issues of sexual violence and unwanted pregnancies associated with the conflict and displacement. This article aims to develop this work further by examining intersecting factors shaping the reproductive health experiences of women internally displaced by the Boko Haram and Herdsmen crisis in Northern Nigeria. To this end, a critical ethnography study involving in-depth interviews with 29 internally displaced women and five service providers in Northern Nigeria was completed between May 2019 to September 2019. Three major intersected subjects pertaining to women’s reproductive health access were observed. These were: (1) normative perceptions and the prevalence of urogenital infections; (2) decisions made on birthplaces and number of births; (3) income and accessibility to care. The findings illustrate the interrelated economic and sociocultural factors that constrain access to reproductive health for internally displaced women.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".