The Shadow Pandemic: A Qualitative Exploration of the Impacts of COVID-19 on Service Providers and Women Survivors of Intimate Partner Violence and Brain Injury
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) affects up to 1 in 3 women over their lifetime and has intensified during the COVID-19 pandemic. Although most injuries are to the head, face, and neck, the intersection of IPV and brain injury (BI) remains largely unrecognized. This article reports on unexplored COVID-19-related impacts on service providers and women survivors of IPV/BI. OBJECTIVES: To explore the impact of the COVID-19 pandemic on survivors and service providers. PARTICIPANTS: Purposeful sampling through the team's national Knowledge-to-Practice (K2P) network and snowball sampling were used to recruit 24 participants across 4 categories: survivors, executive directors/managers of organizations serving survivors, direct service providers, and employer/union representatives. DESIGN: This project used a qualitative, participatory approach using semistructured individual or group interviews. Interviews were conducted via videoconferencing, audio-recorded, and transcribed. Transcripts were thematically analyzed by the research team to identify themes. FINDINGS: COVID-19 has increased rates and severity of IPV and barriers to services in terms of both provision and uptake. Three main themes emerged: (1) implications for women survivors of IPV/BI; (2) implications for service delivery and service providers supporting women survivors of IPV/BI; and (3) key priorities. Increased risk, complex challenges to mental health, and the impact on employment were discussed. Adaptability and flexibility of service delivery were identified as significant issues, and increased outreach and adaptation of technology-based services were noted as key priorities. CONCLUSIONS: The COVID-19 pandemic has intensified IPV/BI, increased challenges for women survivors and service providers, and accentuated the continued lack of IPV/BI awareness. Recommendations for service delivery and uptake are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".