Forward With Dementia: Co‐designing resources adapted for the Chinese, South Asian and Italian communities
Bibliographic record
Abstract
BACKGROUND: Stigma is a common experience for people living with dementia, is associated with many negative consequences including discrimination and social exclusion, and can serve as a barrier to accessing services for people living with dementia. Unfortunately, dementia-related stigma may be even more likely to occur in ethno-racial communities. Dementia resources that are culturally relevant to ethno-racial communities and written in the languages read in these communities are lacking. Forward With Dementia (FWD) is an initiative that aims to combat stigma and raise awareness about living well with dementia. To begin to address the gap in culturally relevant information and resources, the Canadian FWD team co-designed resources that were adapted for the Chinese, South Asian and Italian communities. METHOD: Co-design teams were established for each ethno-racial community. Teams included family/friend care partners and health and social care providers. Unfortunately, we were unable to recruit people living with dementia to participate, likely due to increased dementia-related stigma and the tendency of later diagnosis in these communities. Content from the existing FWD site served as the basis for the resources. We met monthly with each co-design team to adapt and review the resources. Pictures, symbols, personal stories, and examples relevant to each community were used in the design of the resources. Once finalized, the resources were translated into relevant languages. RESULT: Over a 1-year period, a total of 187 newly adapted resources were created. This included 13 core resources and 1-2 stories by care partners for each community. Chinese resources were translated into Simplified and Traditional Chinese, South Asian resources were translated into Punjabi, Hindi and Urdu, and Italian resources were translated into Italian. The resources are also available in English and French. During a 3-month marketing campaign to promote the resources, over 157,000 individuals were reached with 4300+ views of the resources and 1400+ downloads. CONCLUSION: The new culturally-adapted and translated FWD resources address a significant gap in support for people with dementia and care partners from ethno-racial communities.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".