Hubs of Expressive Arts for Life (HEAL) as an intervention to support newcomer survivors of gender based domestic violence
Bibliographic record
Abstract
Access Alliance Multicultural Health and Community Services (Access Alliance) will work with peer researchers, community members as well as academic and community organization partners to implement HEAL, an inter-sectoral, creative, culturally safe, multi-pronged capacity enriching project. This co-design, mixed method, community based participation action research is focused on vulnerable newcomer populations who are survivors of domestic violence in the City of Toronto. The team will develop expressive arts interventions and identify promising or best practices to address the trauma-informed health impacts of family violence and to improve participants’ physical and mental wellbeing. Implementation groups include Arabic, Bengali, Dari and/or Farsi, Tigrinya and/or Amharic speaking; LGBTQ+ asylum seekers; and women living in shelters. Research questions: 1. Which modalities of Expressive Arts Therapy practices can improve health and wellbeing of gender-based domestic violence survivors? 2. To identify the baseline needs of newcomer survivors of domestic based violence. 3. What changes can we expect and measure in participant's attitudes, knowledge, and practices during and after their participation in the Expressive Arts Therapy program? 4. How can successful interventions be scaled up and shared with other organizations? Objectives: - Deliver interdisciplinary team based expressive arts programs to newcomer women - Develop art medium tools and processes to build upon a distilled expressive arts guidebook with trauma and violence informed best practices. - Apply co-design and mixed method research to measure the impact the expressive art intervention has on participant's awareness of mental health and support services. - Mobilize new evidence learned and recalibrated methodologies to contribute to community of practice.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".