Mobile Phone Apps for Intimate Partner and Sexual Violence Prevention and Response: Systematic Search on App Stores
Bibliographic record
Abstract
BACKGROUND: Since the 2008 advent of the smartphone, more than 180 billion copies of apps have been downloaded from Apple App Store, with more than 2.6 million apps available for Android and 2.2 million apps available for iOS. Many violence prevention and response apps have been developed as part of this app proliferation. OBJECTIVE: This study aims to evaluate the prevalence and quality of freely available mobile phone apps targeting intimate partner violence (IPV) and sexual violence (SV) prevention and response. METHODS: We conducted a systematic search of violence prevention and response mobile phone apps freely available in Apple App Store (iOS; March 2016) and Google Play Store (Android; July 2016). Search terms included violence prevention, sexual assault, domestic violence, intimate partner violence, sexual violence, forensic nursing, wife abuse, and rape. Apps were included for review if they were freely available, were available in English, and had a primary purpose of prevention of or response to SV or IPV regardless of app target end users. RESULTS: Using the Mobile Application Rating Scale (MARS), we evaluated a total of 132 unique apps. The majority of included apps had a primary purpose of sharing information or resources. Included apps were of low-to-moderate quality, with the overall subjective quality mean for the reviewed apps being 2.65 (95% CI 2.58-2.72). Quality scores for each of the 5 MARS categories ranged from 2.80 (engagement) to 4.75 (functionality). An incidental but important finding of our review was the difficulty in searching for apps and the plethora of nonrelated apps that appear when searching for keywords such as "rape" and "domestic violence" that may be harmful to people seeking help. CONCLUSIONS: Although there are a variety of mobile apps available designed to provide information or other services related to SV and IPV, they range greatly in quality. They are also challenging to find, given the current infrastructure of app store searches, keyword prioritization, and highlighting based on user rating. It is important for providers to be aware of these resources and be knowledgeable about how to review and recommend mobile phone apps to patients, when appropriate.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".