Harsh Truth: Do Healthcare Providers Silence Women who Experience Intimate Partner Violence
Bibliographic record
Abstract
Background: Globally, one-third of women experience abuse from their intimate partners. Since intimate partner violence (IPV) creates a chronic stress environment, affected women suffer from several physical and mental stress-related disorders for which they seek healthcare services in higher proportion to that of non-abused women. Although affected women seek help for the consequences of IPV, addressing the cause, is an important responsibility of healthcare providers. This study aimed to explore how healthcare providers may contribute to silencing of women who have experienced IPV. Subjects and Methods: This was an integrative review. We performed a systematic search of eight databases for articles published between 2007 and 2018. We identified 4507 publications. We included the English language articles that focused on adult women between 18 and 49 years of age, explored the issue of silencing of women who have experienced IPV, and followed a primary research study design. Two reviewers screened the articles using the web application, Rayyan. Quality was assessed using Joanna Briggs Institute Critical Appraisal tools. Results: Five articles were selected for analysis. The findings revealed that healthcare providers might play a significant role in silencing women's suffering from abuse. Affected women's unwillingness to act as their own agent in healthcare settings or disclose experiences of IPV was associated with healthcare provider's inadequate or inappropriate response. Lack of affirmation, validation, and inability to make women feel accepted were the main factors which silenced women who experienced IPV. Both individual-level factors, such as shame, fear, humiliation, hope, and relationship dynamics, and community-level factors, such as cultural norms, and values, seemed to precede the factors related to healthcare providers. Conclusion:A socio-ecological understanding of the factors influencing silencing of women who have experienced IPV is required. A health care model which takes into consideration the contributing factors at various ecological levels of influence is imperative to guide healthcare providers towards the development of best practices in caring with women who have experienced violence in their intimate relationships.
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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 | 0.034 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| 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".