Co-design of a supportive movement/dance program with Body Brave for individuals affected by eating disorders
Bibliographic record
Abstract
BackgroundEating disorders (EDs) are prevalent and have the highest mortality rate of any mental illness.Access to publicly funded specialised treatment requires a referral from primary care and may be inappropriate for some affected individuals.Community-based support services supplement treatment and provide unconventional therapy.Body Brave, a charitable organisation in Hamilton, Ontario, Canada that provides innovative ED support and treatment services to affected adults and caregivers, is one such service.While the literature suggests that movement or dance interventions may provide physical, social, and therapeutic benefits for individuals with EDs, Body Brave does not currently offer such a program. AimsDesigning a movement or dance program that supports individuals affected by EDs is an opportunity for service design with Body Brave.Therefore, our design research question was: How might a movement or dance program and environment be designed and implemented within Body Brave to support well-being for clients affected by EDs? Method This co-design project involved three elements: a literature review within the fields of eating disorders, dance and health, dance movement therapy, and environmental design; interviews with ED subject matter experts (SMEs); and co-design sessions with Body Brave staff.The project received Research Ethics Board approval from the Ontario College of Art and Design (OCAD)
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".