EXPLORING IKIGAI AND LIVING WELL WITH DEMENTIA AMONG JAPANESE CANADIANS: A NARRATIVE AND ARTS-BASED RESEARCH STUDY
Bibliographic record
Abstract
Abstract Like many ethnocultural minority populations, Japanese Canadians have been underrepresented in research pertaining to experiences of living with dementia. Representation of how diverse populations experience living with dementia is necessary to further scholarship on the intersections of dementia and culture. Thus, we aim to explore how Japanese Canadians live well with dementia through the conceptual lens of ikigai, a Japanese wellbeing construct. In this two-phase study, we took a relational approach and recruited 4 people living with dementia and 3 of their care partners from the Japanese Canadian community. In the first phase, we conducted individual and dyadic narrative interviews to understand experiences of living well with dementia. In the second phase, we gathered in a group art-making workshop to enable participants to identify and express what they wanted others to know about how they live well with dementia. To ensure that individuals could participate meaningfully in their preferred language, the study was conducted in English and Japanese. Audio recordings from both phases were transcribed and analyzed through narrative inquiry. Through our study, we identified several ways Japanese Canadians maintained their ikigai to live well with dementia, including notions of balance, continuity, and gratitude. Our findings offer insights into the integral role of culture in living well with dementia, as well as the value of culturally nuanced and inclusive research.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.034 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".