“Don’t Turn a Blind Eye”: An Instruction for Supporting Meaningful Conversations About Gender-Based Violence During Perinatal Care
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Evidence suggests that Gender-based violence (GBV) is prevalent throughout the perinatal period. Women during this time have frequent contact with healthcare providers (HCPs), and there are many opportunities that HCPs can identify GBV and support women by early intervention during routine prenatal care. However, evidence shows that HCPs are still hesitant to address this issue. This study was conducted to explore the experiences of Survivors and HCPs on how to manage a meaningful conversation about GBV with survivors during perinatal care. METHODS: A thematic approach has been used in this qualitative study. RESULTS: Twenty-eight semi-structured interviews were conducted with survivors and HCPs. Three main themes emerged from the data analysis, including: "Knock gently on the door to enter the client's private world", "Show interest in clients' stories that are beyond their physical problems" and "Gradually and cautiously cross the hidden borders." CONCLUSION: HCPs play a pivotal role in identifying GBV and providing support for survivors, particularly during their perinatal period. However, initiating a conversation around this sensitive topic needs time, skill, and enough knowledge. Validating survivors' experiences, providing a private and safe atmosphere without judgment, and creating empathy could lead to more disclosure of GBV. To have a meaningful conversation, HCPs need to have a holistic approach toward care, show interest in clients' stories beyond their physical problems, and support clients who have shared sensitive information.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".