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Record W4385827286 · doi:10.2196/45917

Feasibility and Effectiveness of an Intervention to Reduce Intimate Partner Violence and Psychological Distress Among Women in Nepal: Protocol for the Domestic Violence Intervention (DeVI) Cluster-Randomized Trial

2023· article· en· W4385827286 on OpenAlexvenueno aff
Rachana Shrestha, Diksha Sapkota, Devika Mehra, Anna Mia Ekström, Keshab Deuba

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
FundersVetenskapsrådet
KeywordsDomestic violenceMental healthRandomized controlled trialMedicineCluster randomised controlled trialPsychiatryPoison controlSuicide preventionGeneral Health QuestionnaireIntervention (counseling)Environmental health

Abstract

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BACKGROUND: Intimate partner violence (IPV) disproportionately affects people in low-and middle-income countries (LMICs), such as Nepal. Women experiencing IPV are at higher risk of developing depression, anxiety, and posttraumatic stress disorder. The shortage of trained frontline health care providers, coupled with stigma related to IPV and mental health disorders, fuels low service uptake among women experiencing IPV. The Domestic Violence Intervention (DeVI) combines the Problem Management Plus counseling program developed by the World Health Organization with a violence prevention component. OBJECTIVE: This study aims to implement and evaluate the feasibility, acceptability, and effectiveness of DeVI in addressing psychological distress and enabling the secondary prevention of violence for women experiencing IPV. METHODS: A parallel cluster-randomized trial will be conducted across 8 districts in Madhesh Province in Nepal, involving 24 health care facilities. The study will include women aged 18-49 years who are either nonpregnant or in their first trimester, have experienced IPV within the past 12 months, have a 12-item General Health Questionnaire (GHQ-12) score of 3 or more (indicating current mental health issues), and have lived with their husbands or in-laws for at least 6 months. A total sample size of 912 was estimated at 80% power and α<.05 statistical significance level to detect a 15% absolute risk reduction in the IPV frequency and a 50% reduction in the GHQ-12 score in the intervention arm. The health care facilities will be randomly assigned to either the intervention or the control arm in a 1:1 ratio. Women visiting the health care facilities in the intervention and control arms will be recruited into the respective arms. In total, 38 participants from each health care facility will be included in the trial to meet the desired sample size. Eligible participants allocated to either arm will be assessed at baseline and follow-up visits after 6, 17, and 52 weeks after baseline. RESULTS: This study received funding in 2019. As of December 29, 2022, over 50% of eligible women had been recruited from both intervention and control sites. In total, 269 eligible women have been enrolled in the intervention arm and 309 eligible women in the control arm. The trial is currently in the recruitment phase. Data collection is expected to be completed by December 2023, after which data analysis will begin. CONCLUSIONS: If the intervention proves effective, it will provide evidence of how nonspecialist mental health care providers can address the harmful effects of IPV in resource-constrained settings with a high burden of IPV, such as Nepal. The study findings could also contribute evidence for integrating similar services into routine health programs in LMICs to prevent IPV and manage mental health problems among women experiencing IPV. TRIAL REGISTRATION: ClinicalTrials.gov NCT05426863; https://clinicaltrials.gov/ct2/show/NCT05426863. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45917.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0310.004

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.

Opus teacher head0.224
GPT teacher head0.604
Teacher spread0.380 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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