Mobile Apps to Prevent Violence Against Women and Girls (VAWG): Systematic App Research and Content Analysis
Bibliographic record
Abstract
Background: Numerous reviews have explored specific aspects of violence prevention apps, but given the rapid development of new apps, increased violence during COVID-19, and gaps in understanding functionalities and geographical distribution, an updated review is needed. Objective: Therefore, we aimed to systematically evaluate the trends, geographical distribution, functional categories, available features, and feature evolution of mobile apps designed to prevent violence against women and girls (VAWG). Methods: We conducted a systematic search on app reselling platforms and search engines from April 24, 2024 to May 28, 2024, using terms related to VAWG in multiple languages. We included apps meeting our criteria for addressing VAWG, without restrictions on date or language. We conducted content analysis of app and apps were categorized by functionality and feature type. We performed descriptive analyses, trend analysis, co-occurrence network analysis, and geographical mapping. Results: Out of 432 apps initially identified, 178 were included in the final analysis. Of these, 99 apps were available on both Google Play and the App Store, and 64 were exclusive to Google Play. Most apps were implemented in North America (48/178, 27%), followed by South Asia (31/178, 17%) and Europe and Central Asia (31/178, 17%). Emergency and support apps were most prevalent across regions. Most apps (132/178, 74%) originated from the private sector and were designed for survivor (121/178, 68%), were free without in-app purchases (100/178, 56%), had a website (148/178, 83%), and offered GPS features (142/178, 80%), but only 15% (27/178) provided offline functionality. App releases peaked in 2020 (33/178, 19%), followed by a decline. Regression analysis indicated a significant trend (P=.01) increase in app release, with a 2.40 unit increase per year before 2020 and a 7.01 unit decrease after, showing a post-2020 decline of 4.61 units per year. Apps were primarily categorized as emergency (n=110) or support (n=81), with most emergency apps in the 10,000 to ≥100,000 downloads range. Network analysis showed that emergency services (degree=10, clustering coefficient=0.911), location sharing (degree=10, clustering coefficient=0.911), SOS (Save Our Souls) alerts (degree=10, clustering coefficient=0.911), and educational resources (degree=10, clustering coefficient=0.911) features highly co-occurred in the same app. We found a gradual shift towards more sophisticated and comprehensive safety tools, evolving from basic GPS tracking and SOS alerts to advanced features such as real-time communication, panic buttons, peer support, and group communication, culminating in multifunctional platforms offering personalized safety, community engagement, and proactive risk identification. Conclusions: Most apps to prevent VAWG emphasize emergency and support functions, and although initial releases increased, there has been a recent decline, with a shift towards integrating more comprehensive safety solutions such as communication, reporting, and community engagement. Future app development should prioritize cross-platform availability, offline functionality, public sector collaboration, and the integration of advanced technologies like artificial intelligence.
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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 | 0.030 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.040 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".