Applying Mobile Technology to Address Gender-Based Violence in Rural Nigeria: Experiences and Perceptions of Users and Stakeholders
Bibliographic record
Abstract
This paper documents the results of an intervention conducted in Nigeria to test the effectiveness of a mobile phone technology, text4life, in enabling women to self-report gender-based violence (GBV). Women experiencing GBV and other challenges related to sexual and reproductive health and rights were requested to use their mobile phones to text a code to a central server. In turn, the server relayed the messages to trained nearby health providers and civil society organization (CSO) officials who reached out to provide health care and social management services to the callers. Interviews were conducted with some callers, health care providers, and CSO staff to explore their experiences with the device. The interviews and data from the server were analyzed qualitatively and quantitatively. The results indicate that over a 27-month period, 3,403 reports were received by the server, 34.9% of which were reporting GBV. While interviewees perceived that a large proportion of the women were satisfied with the use of text4life, and many received medical treatment and psychological care, the consensus opinion was that many women reporting GBV did not wish to pursue police or legal action. This was due to women’s perceptions that there would be negative cultural and social backlash should they pursue civil punishments for their partners. We conclude that a mobile phone device can be used effectively to report GBV in low-resource settings. However, the device would be more useful if it contributes to equitable primary prevention of GBV, rather than secondary prevention measures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".