Attending to Power: Stakeholder Perspectives on Training Physicians to Address Intimate Partner Violence
Bibliographic record
Abstract
Intimate partner violence (IPV) is associated with a wide range of mental and physical health concerns. Research suggests that many physicians lack knowledge and skills to adequately respond to patients experiencing IPV. In order to better integrate physicians’ contributions into intersectoral responses to IPV, we asked stakeholders with expertise and experience related to IPV about the knowledge, skills, attitudes, and behaviors they wanted them to have. Guided by principles of interpretive description, and using a key informant method, we conducted unstructured interviews with 18 stakeholders in IPV-related frontline, managerial, or policy roles in Ontario, Canada. Data collection and analysis proceeded iteratively through 2022; “thoughtful practitioners” outside the research team were recruited at key junctures to provide feedback on formative findings. Stakeholders suggested that “attending to power” should be a core principle for medical practice related to IPV. Attending to power encompassed understanding interactional, organizational, and structural power dynamics related to IPV and purposefully engaging with power, by taking action to empower people subjected to violence. Specific recommendations for practice concerned four focal contexts: relationships between partners, between patients and providers, between providers, and in social systems and structures. Strengthening physicians’ capacity to attend to power dynamics relevant to their IPV practice is an important step in both improving medical care for people experiencing IPV and integrating physician contributions into other services and supports.
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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 | 0.038 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.022 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| 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".