Exploring 2S/LGBTQIA+ People’s Experiences with Intimate Partner Violence During the COVID-19 Pandemic in Ontario: A Multi-Methods Qualitative Study
Bibliographic record
Abstract
Intimate partner violence (IPV) involves aggressive or abusive behaviour that harms or intimidates a current or former romantic partner. Although sexual and gender diverse (2S/LGBTQIA+) people may disproportionately experience IPV, their experiences are not well documented in the Canadian context. This multi-methods qualitative study documents 2S/LGBTQIA+ survivors’ experiences with IPV and access to related services throughout the COVID-19 pandemic via in-depth interviews with survivors and service providers. Survivors experienced multiple, concurrent forms of abuse that contributed to poor mental health outcomes, both of which were intensified by the COVID-19 pandemic. Survivors had difficulty recognizing themselves as victims and were unaware of services. Services are insufficiently funded and unable to meet the needs of their communities. Gender-based violence organizations want to serve transgender women and transfeminine people but second-wave feminist frameworks in policy and funding mechanisms are a barrier to expanding services. Service providers need predictable, annualized funding, must improve outreach, and shift to an intersectional feminist framework that includes 2S/LGBTQIA+ people. Comprehensive sexual health education and regular IPV screenings by mental health professionals are crucial for IPV prevention.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".