Co‐Designing Peer Support for Women Labelled With Intellectual Disabilities Who Have Experienced Sexual Violence
Bibliographic record
Abstract
BACKGROUND: Women labelled with intellectual disabilities face high rates of social exclusion and sexual violence, with limited research and few services tailored to their needs. Peer support offers many benefits including recognition of diverse lived experiences and nonprofessional perspectives. This article highlights collaborative efforts of women labelled with intellectual disabilities, service providers and researchers in advancing peer support through an applied research project. METHODS: Using trauma-informed, arts-based and human-rights-focused co-design approaches, we fostered inclusive research and programme development in a 3-day virtual symposium. RESULTS: The study emphasises shared control of research agendas, shaped by a multistakeholder team prioritising lived experience knowledge. Women took more control over the research through co-creative practices to that meaningfully included them in the design of future research and programming. CONCLUSION: This inclusive model champions equality and equity, advocating for positive discourse around intellectual disabilities while bridging lived, academic and professional insights in the field.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".