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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".