Perspectives on delivering safe and equitable trauma-focused intimate partner violence interventions via virtual means: A qualitative study during COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has been linked with increased rates of intimate partner violence (IPV) and associated experiences of compounded trauma. The emergence of this global pandemic and the public health measures introduced to limit its transmission necessitated the need for virtually delivered interventions to support continuity of care and access to interventions for individuals affected by IPV throughout the crisis. With the rapid shift to virtual delivery, understanding the barriers to accessing virtually delivering trauma-focused IPV interventions to these individuals was missed. This study aimed to qualitatively describe the challenges experienced by service providers with delivering virtually delivered IPV services that are safe, equitable, and accessible for their diverse clients during the COVID-19 pandemic. METHODS: The study involved semi-structured interviews with 24 service providers within the anti-violence sector in Alberta, Canada working with and serving individuals affected by IPV. The interviews focused on the perspectives and experiences of the providers as an indirect source of information about virtual delivery of IPV interventions for a diverse range of individuals affected by IPV. Interview transcripts were analyzed using inductive thematic analysis. RESULTS: Findings in our study show the concepts of equity and safety are more complex for individuals affected by IPV, especially those who are socially disadvantaged. Service providers acknowledged pre-existing systemic and institutional barriers faced by underserved individuals impact their access to IPV interventions more generally. The COVID-19 pandemic further compounded these pre-existing challenges and hindered virtual access to IPV interventions. Service providers also highlighted the pandemic exacerbated structural vulnerabilities already experienced by underserved populations, which intensified the barriers they face in seeking help, and reduced their ability to receive safe and equitable interventions virtually. CONCLUSION: The findings from this qualitative research identified key determining factors for delivering safe, equitable, and accessible virtually delivered intervention for a diverse range of populations. To ensure virtual interventions are safe and equitable it is necessary for service providers to acknowledge and attend to underlying systemic and institutional barriers including discrimination and social exclusion. There is also a need for a collaborative commitment from multiple levels of the social, health, and political systems.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".