Interventions to Reduce Stigma of Dementia: First Insights From a Rural Community-Based Participatory Study
Bibliographic record
Abstract
Abstract Age is the greatest risk factor for dementia, and the number of rural older adults is rising. Although dementia-related stigma is widely documented, few studies focus on ways to reduce stigma, especially within rural communities. This late breaker presentation aims to: 1) explore the contributing factors of dementia-related stigma in rural communities; and 2) identify interventions to reduce stigma of dementia in rural communities. Drawing on a community-based participatory approach, data were collected through semi-structured interviews with 18 older adults, and a focus group with 7 community leaders in rural Saskatchewan, Canada. Thematic analysis was used to identify key themes and patterns within the data. Contributing factors of dementia-related stigma ranged from fear to lack of dementia knowledge. Several anti-stigma interventions were identified including: forming support groups; hosting educational workshops; inviting guest speakers with dementia; talking openly about dementia; learning more about dementia; asking questions; sharing your lived-experiences; being inclusive; developing inter-generational programs; and avoiding assumptions and hurtful jokes. As the rural population ages, there is a growing need for interventions, programs, and policies to address stigma of dementia. Engaging in rural partnerships and collaborative research is essential to developing community-informed strategies to reduce dementia-related stigma and improve the quality of life for people with dementia.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".