Engaging women in health and justice research: A North Wales multi-agency approach
Bibliographic record
Abstract
Women at risk of entering the criminal justice system (CJS) often face significant disparities, such as complex health needs and experiences of trauma, yet remain underrepresented in research. This paper presents a participatory, multi-agency methodology for conducting inclusive research with this group in North Wales. Developed through collaboration between health, justice, and third-sector organizations, the approach sought to build trust, enhance engagement, and generate a richer understanding of women’s health experiences. Drawing on practitioner-informed design, trauma-informed data collection, and community-based settings, the project demonstrates how cross-sector partnerships can overcome traditional barriers to participation. The paper reflects on how this model facilitated participation from a population that typically finds it difficult to engage in the research process whilst strengthening women’s trust and confidence. Findings highlight the potential of collaborative, place-based research as a social innovation for building inclusion, improving knowledge translation, and informing whole-system responses to women’s health and justice needs.
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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 | 0.045 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".