AMPLIFYING VOICES: DIGITAL STORYTELLING TO EXPLORE AGING IN THE RIGHT PLACE WITH HOUSING PRECARIOUS OLDER ADULTS
Bibliographic record
Abstract
Abstract Digital storytelling (DS) offers an innovative qualitative approach to explore the intricate narratives surrounding aging in the right place (AIRP) for older adults with experiences of housing precarity. We engaged three participants as co-researchers to co-create a story of their life-course narratives through participation in a photovoice study followed by the development of a DS video. Study participants live in affordable rental housing with onsite supports in Metro Vancouver, Canada. Central to our DS methods is the co-creation format where our co-researchers actively shape the narrative through qualitative interviews, photographs, and video footage of the socio-spatial context of their housing highlighting the multifaceted dimensions of AIRP. The integration of photovoice photographs within the DS serves as a visual anchor, providing nuanced insights into participants’ lived experiences and aspirations regarding their current and ideal living environments. Their stories demonstrate that a combination of social, environmental, and organizational factors, as well as an interweaving of objective and subjective aspects of their housing and service experiences contribute to their stability, social integration, and well-being as they AIRP. This type of co-creative project not only serves as a means of knowledge mobilization, but also fosters dialogue and reflection between co-researchers, service providers and researchers. DS methods have the transformative potential to amplify the voices of marginalized older adults, highlighting their unique housing experiences and advocating for inclusive policy measures that prioritize their diverse needs.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".