CREATING DESIGN CONNECTIONS: ARTS-BASED METHODS AS A PATHWAY INTO TRAUMA-INFORMED DESIGN FOR OLDER HOMELESS ADULTS
Bibliographic record
Abstract
Abstract Older adults who have experienced homelessness are more likely to live with trauma than their continually housed counterparts. Trauma-informed design (TID) recognizes that the built environment – defined as all physical elements that are human-made or curated to respond to human needs, desires, or purposes – impacts the physical, psychological, and emotional effects of trauma. TID seeks to positively shape this trauma-built environment connection through intentional design. While environmental gerontology has long understood the connection between aging well and the built environment, understanding trauma-informed design for aging adults is a new perspective. In this presentation, we explore the importance of collaborative and artful methodological strategies for understanding the TID needs of older adults with experiences of homelessness, generated by a secondary data analysis of 35 arts-based interviews with older adults (ages 50-71) living in four supportive housing sites in Calgary, Canada. We share our methods (photovoice and arts-based elicitation), examples of artful knowledge concerning the built environment created by research participants, and insights for translating art-based findings into design strategies, using our own research project as an exemplar. Overall, we demonstrate the possibilities of arts-based methods to understand, design, and create trauma-informed supportive housing for older adults with experiences of homelessness that fosters wellness, slows aging, and promotes healing from trauma.
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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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".