Promoting resilience in adults with experience of intimate partner violence or child maltreatment: a narrative synthesis of evidence across settings
Bibliographic record
Abstract
BACKGROUND: People who have experienced intimate partner violence (IPV) or child maltreatment (CM) are at risk of having lower resilience and adverse psychological outcomes. In keeping with the social and environmental factors that support resilience, there is a need to take a public health approach to its investigation and to identify existing initiatives in particular settings and populations that can guide its deliberate promotion. METHOD: This narrative synthesis examines quantitative and qualitative studies of interventions with resilience-related outcomes in specified health and other settings. Clinical RCTs are excluded as beyond the scope of this review. RESULTS: Twenty studies were identified for review in several settings, consisting of 14 quantitative studies, 2 review studies, 2 qualitative studies and 2 mixed-methods studies. Three quantitative studies produced strong evidence to support: a home visitation program for at-risk mothers; a methadone program for women and a substance abuse program. This review reveals that few studies use specific resilience measures. CONCLUSIONS: The topic has been little studied despite high needs for public health interventions in countries of all types. Interventions and research studies that use specific resilience measures are likely to help measure and integrate what is currently a disparate area. IMPLICATIONS: The participation of people with IPV or CM history in program and research design and implementation is indicated to support advocacy, innovation and sustainable interventions. This is especially pertinent for interventions in LAMIC and indigenous settings where continuing programs are sorely needed.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".