A Community-Based Workshop on Addressing Dementia-Related Stigma: First Insights From a Rural Community
Bibliographic record
Abstract
Abstract Dementia-related stigma can delay early dementia diagnosis and lead to social isolation, depression, and suicide. Despite this knowledge, few studies identify strategies to reduce dementia-related stigma. This late-breaker poster begins to address this gap by showcasing the educational components of a community-based workshop to share study findings on reducing dementia-related stigma in rural communities. Guided by solutions-focused theory, semi-structured interviews were conducted with 18 seniors including family members, friends, caregivers and people affected by dementia and other forms of cognitive impairment in rural Saskatchewan, Canada. A focus group was conducted with 7 rural community leaders. The interview and focus group transcripts were analyzed using thematic analysis. Based on the interview and focus group findings, educational components of the workshop included: a dementia definition, different dementia types, warning signs/symptoms, risk reduction strategies, and information on dementia-related stigma and myths. Several strategies to reduce stigma were identified ranging from hosting inter-generational programs to inviting guest speakers with dementia. This study was found to be beneficial for improving knowledge, attitudes, comfort levels, and awareness of dementia. Additional research is needed to develop, implement, and evaluate interventions to reduce dementia-related stigma in different cultures and contexts.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".