ICT-Based Interventions for Women Experiencing Intimate Partner Violence: Research Needs in Usability and Mental Health
Bibliographic record
Abstract
BACKGROUND: Intimate Partner Violence is a "global pandemic". Meanwhile, information and communication technologies (ICT), such as the internet, mobile phones, and smartphones, are spreading worldwide, including in low- and middle-income countries. We reviewed the available evidence on the use of ICT-based interventions to address intimate partner violence (IPV), evaluating the effectiveness, acceptability, and suitability of ICT for addressing different aspects of the problem (e.g., awareness, screening, prevention, treatment, mental health). METHODS: We conducted a systematic review, following PRISMA guidelines, using the following databases: PubMed, PsycINFO, and Web of Science. Key search terms included women, violence, domestic violence, intimate partner violence, information, communication technology, ICT, technology, email, mobile, phone, digital, ehealth, web, computer, online, and computerized. Only articles written in English were included. RESULTS: Twenty-five studies addressing screening and disclosure, IPV prevention, ICT suitability, support and women's mental health were identified. The evidence reviewed suggests that ICT-based interventions were effective mainly in screening, disclosure, and prevention. However, there is a lack of homogeneity among the studies' outcome measurements and the sample sizes, the control groups used (if any), the type of interventions, and the study recruitment space. Questions addressing safety, equity, and the unintended consequences of the use of ICT in IPV programming are virtually non-existent. CONCLUSIONS: There is a clear need to develop women-centered ICT design when programming for IPV. Our study showed only one study that formally addressed software usability. The need for more research to address safety, equity, and the unintended consequences of the use of ICT in IPV programming is paramount. Studies addressing long term effects are also needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".