Dementia Advocacy in Action: Examining Social Media During World Alzheimer’s Month
Bibliographic record
Abstract
Abstract Dementia awareness campaigns play a vital role in reducing stigma and improving the quality of life of people with dementia. However, there is a paucity of research on advocacy strategies to support dementia awareness, especially on social media. This presentation aims to: i) identify dementia advocacy strategies used to increase awareness on social media during World Alzheimer’s Month; and ii) understand the role of social media in dementia awareness campaigns. Using data from the social media platform X, posts were scraped from September 1 to September 30, 2022. Filters were used to screen for duplicate posts, non-English content, and reply posts with missing content. The remaining 1,981 relevant posts were examined using thematic analysis to identify prominent dementia advocacy strategies. During our data analysis, steps were taken to support rigor such as using a standardized codebook, practice exercises, intercoder reliability checks, and team discussions to oversee coding disagreements. Based on our analysis, we identified four main advocacy strategies: i) myth-busting to address false information; ii) political advocacy to support dementia care reform; iii) tailoring messages to ethnic and cultural groups; and iv) amplifying personal narratives and lived experience of dementia. Although a range of strategies were identified, further research is needed to evaluate the effectiveness of awareness campaigns in addressing dementia-related stigma. The findings from our study have implications for community leaders, health professionals, and policymakers who are working to increase dementia awareness. Our findings provide vital insight to enhance national-level dementia advocacy and awareness campaigns on social media.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.007 |
| 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".