Stigma of Dementia During COVID-19: First Insights From a Twitter Study
Bibliographic record
Abstract
Abstract Stigma is a critical issue that reduces the quality of life for people living with dementia and their care partners. Despite this knowledge, little research examines stigma of dementia, especially within the context of the COVID-19 pandemic. This presentation aims to: 1) identify the contributing factors of stigma against dementia during the COVID-19 pandemic; and 2) describe actions to challenge stigma of dementia. Using Twitter data, tweets were compiled with Python’s GetOldTweets application from February to September 2020. Search terms included keywords for dementia (e.g., Alzheimer’s) and COVID-19 (e.g., coronavirus). From the 20,800 tweets, filters were used to exclude irrelevant tweets. The remaining 5,063 tweets were analyzed by a group of coders with 1,743 tweets identified for further stigma-related coding. The 1,743 tweets were exported to Excel for thematic analysis and divided among 13 coders. Each tweet was coded independently by two reviewers to ensure intercoder reliability (e.g., 86%). Contributing factors of stigma of dementia included: ageism and devaluing the lives of people with dementia (e.g., ‘old and dying anyways’); misinformation and false beliefs (e.g., ‘COVID-19 vaccine causes dementia’); political dementia-related slander and ridicule (e.g., ‘dementia Joe’); and stigma within healthcare and long-term care organizations (e.g., pushing DNR orders). Globally, there is an urgent need for more dementia education and awareness targeted towards the general public, healthcare workers, and policymakers to reduce stigma against people living with dementia. Further research is necessary to explore the contributing factors and interventions to reduce stigma of dementia during the COVID-19 pandemic and beyond.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".