Family and Domestic Violence Priority Setting Partnership Project Report
Bibliographic record
Abstract
The top ten priorities established for future research in family and domestic violence:1. Law, courts and violence restraining orders• What are the barriers to victims in the legal and court process (including adequate representation, court processes, attitudes of magistrates and lawyers, time limitations, relevance of laws, professional silos, review processes)?• What support is available in the court process for victims of family and domestic violence and how could this be improved? Police• How can police better respond to the needs of victims (e.g.Violence Restraining Orders, responses and breaches, training, incident reports, collaboration with human services, handover issues)?3. Non-physical abuse• How do we educate frontline service providers about non-physical abuse and best support clients (not exposed to physical/sexual violence)?• How do we educate the community to recognise non-physical violence as a form of family and domestic violence?• How do women experiencing non-physical violence recognise this as a form of family and domestic violence and access support? Prevention and early intervention*• Are there warning signs that a relationship could result in family and domestic violence?• Are there warning signs that a potential partner could become a perpetrator of family and domestic violence?• What educational strategies could be used to reduce family and domestic violence?• What policies could be implemented to reduce the issues associated with the perpetration of family and domestic violence? Impact on children• What are the long-term psychological effects for children who have been exposed to family and domestic violence?• How do we support children to overcome the effects of exposure to family and domestic violence?6. Mental health issues/outcomes• What are the psychological health effects for adults who have experienced family and domestic violence?• How can we support recovery after family and domestic violence?7. Service delivery• How can services be more accessible, relevant, innovative and culturally appropriate to support a diverse range of victims?• How is the development of services informed by the lived experience? 8. Financial issues• How can we support victims to overcome financial issues associated with family and domestic violence (e.g.Legal Aid/ legal fees, ongoing financial support, relocation costs, living costs, access to ongoing psychological services, work restrictions)?• How can we support victims to overcome financial barriers to leaving a family and domestic violence situation?9. Intergenerational impact and outcomes in family and domestic violence• New theme 10.Perpetrators• What factors are associated with perpetrators of family and domestic violence and how can these be reduced?* Theme expanded at the workshop's final session to include 'early intervention'
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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".