How to Facilitate Disclosure of Violence while Delivering Perinatal Care: The Experience of Survivors and Healthcare Providers
Bibliographic record
Abstract
Gender-based Violence (GBV) during the perinatal period is a serious concern as it is associated with many adverse outcomes for both the mother and the baby. It is well known that violence is under-reported. Thus, official statistics (both police reports and survey data) underestimate the prevalence of violence in general and during the perinatal period specifically. In this study conducted in Canada, we sought to explore the barriers to and facilitators of women disclosing their experiences of GBV within healthcare services to safely facilitate more disclosure in the future and reduce the harms that arise from GBV. We used thematic analysis to analyze in-depth interviews with 16 healthcare providers (nurses, midwives and physicians) and 12 survivors of GBV. The data reflect three main themes: "raising awareness of gender-based violence", "creating a shift in the healthcare system's approach toward gender-based violence" and "providing support for survivors and care providers." Our findings suggest that the healthcare system should increase its investments in raising awareness regarding GBV, training healthcare providers to respond appropriately, and building trust between survivors and healthcare providers. Healthcare providers need to be aware of their role and responsibility regarding identifying GBV as well as how to support survivors who talk about violence. Expanding a relationship-based approach in the care system and providing support for both survivors and health care providers would likely lead to more disclosures.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".