Examining the Effectiveness, Acceptability, and Feasibility of Virtually Delivered Trauma-Focused Domestic Violence and Sexual Violence Interventions: A Rapid Evidence Assessment
Bibliographic record
Abstract
The COVID-19 pandemic has forced a rapid shift to virtual delivery of treatment and care to individuals affected by domestic violence and sexual violence. A rapid evidence assessment (REA) was undertaken to examine the effectiveness, feasibility and acceptability of trauma-focused virtual interventions for persons affected by domestic violence and sexual violence. The findings from this review will provide guidance for service providers and organizational leaders with the implementation of virtual domestic violence and sexual violence-focused interventions. The REA included comprehensive search strategies and systematic screening of and relevant articles. Papers were included into this review (1) if they included trauma-focused interventions; (2) if the intervention was delivered virtually; and (3) if the article was published in the English-language. Twenty-one papers met inclusion criteria and were included for analysis. Findings from the rapid review demonstrate that virtual interventions that incorporate trauma-focused treatment are scarce. Online interventions that incorporate trauma-focused treatment for this at-risk group are limited in scope, and effectiveness data are preliminary in nature. Additionally, there is limited evidence of acceptability, feasibility and effectiveness of virtual interventions for ethnically, culturally, and linguistically diverse populations experiencing domestic violence and sexual violence. Accessing virtual interventions was also highlighted as a barrier to among participants in studies included in the review. Despite the potential of virtual interventions to respond to the needs of individuals affected by domestic violence and/or sexual violence, the acceptability and effectiveness of virtual trauma-focused care for a diverse range of populations at risk of violence are significantly understudied.
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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 | 0.131 | 0.376 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".