A Qualitative Study of Challenges and Opportunities in Mobilizing Research Knowledge on Violence Against Women
Bibliographic record
Abstract
Background Effective delivery of interventions by health and social services requires research-based knowledge which identifies the causes and consequences of violence against women. Methods to effectively share new knowledge with violence against women decision-makers remain under studied. Purpose This paper examines how new research-based knowledge-namely, the lack of efficacy of health-care screening for exposure to intimate partner violence against women-is received by stakeholders in the violence against women field. Methods Data from 10 stakeholder group discussions ( N = 86) conducted during a knowledge-sharing forum were analyzed to assess how stakeholders responded to the new knowledge. Results Participant reactions ranged from full acceptance to significant resistance to the research findings. We suggest themes that help explain these reactions, including the context and content of our findings and their epistemological match to participants' experiences and beliefs, and the perceived value of research evidence, compared to other forms of knowledge. Conclusions Violence against women is a complex psycho-social phenomenon, and people with an interest in this field bring diverse and even conflicting perspectives regarding its causes, consequences, and potential solutions.
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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 | 0.037 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".