Evidence map›Paper›PMID 37457923›Full record

ArticleCentral Asian survey2023

Factors inhibiting institutional responses to domestic violence in Kyrgyzstan.

Saltanat Childress, Nibedita Shrestha, Kendall Anekwe, Mitchell D Wong, Rebecca N Dudovitz

Abstract read
In one paragraph

Article in Central Asian survey, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Saltanat ChildressSchool of Social Work, University of Texas-Arlington, USA.
Nibedita ShresthaSchool of Social Work, University of Texas-Arlington, USA.
Kendall AnekweSchool of Social Work, University of Texas-Arlington, USA.
Mitchell D WongDavid Geffen School of Medicine, University of California, Los Angeles, USA.
Rebecca N DudovitzDavid Geffen School of Medicine, University of California, Los Angeles, USA.

Funding

Training LEADers to Accelerate Global Mental Health Disparities in Research (LEAD)T37MD014218 · NIMHD · WASHINGTON UNIVERSITY · PI CAVAZOS-REHG, PATRICIA A, SSEWAMALA, FRED M · 2019 to 2023
$1.3M
Integrating Evidence-based Approaches to Prevent Child Maltreatment in KyrgyzstanK01HD106070 · NICHD · UNIVERSITY OF TEXAS ARLINGTON · PI Saltanat Childress · 2022 to 2026
$660k
NICHD NIH HHS K01 HD106070NIMHD NIH HHS T37 MD014218
6 · The paper itself

Abstract

The study identifies factors that limit effective institutional responses to domestic violence (DV) in Kyrgyzstan, in the context of recent legislative actions aimed at reducing DV through improvements in law enforcement, judicial processes, and provision of social services. The study uses qualitative, grounded theory methods to analyze interviews and focus groups with 83 professionals working in these sectors. Two major themes emerge from the analysis: (1) barriers to effective institutional responses from internal challenges and constraints, and (2) social resources and challenges identified as important to provide a better collective response. The study highlights the need for capacity development within institutions and broader social learning to overcome existing barriers and better align outcomes with the intentions of recent legislation. Standardized training, awareness-raising, enhanced roles for educators and religious leaders, better coordinated social service provision, rehabilitation for victims and perpetrators, and family-centered school-based interventions are identified as targets for improving responsiveness.

Indexed as

Central Asiadomestic violenceinstitutional responsesintimate partner violenceKyrgyzstan

Identifiers

PMID37457923
PMCPMC10348350

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.