EXAMINING THE HEALTH EQUITY OF PEOPLE WITH DEMENTIA DURING THE COVID-19 PANDEMIC: FIRST INSIGHTS FROM A TWITTER STUDY
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has deepened issues of health inequity and social injustice against people with dementia. Despite having one of the highest mortality rates, little research focuses on the COVID-19 impact of people with dementia. This presentation aims to: 1) explore the COVID-19 experiences and key factors of health inequity among people with dementia during the pandemic; and 2) identify actions to improve the health equity of people with dementia in the pandemic. We collected 6,243 relevant tweets using the Twint application in Python from September 8, 2020, to December 8, 2021. Tweets were divided among eleven coders and analyzed using thematic analysis. Analysis identified three primary themes: structural inequities (e.g., restricted access to health and support services, ageism, social isolation, vaccination barriers, and inadequate staffing in care facilities); frustration and despair due to loss (e.g., loss of cognitive abilities, loss of time with loved ones, and loss of life); and resiliency and hope for the future (e.g., lifting of restrictions and COVID-19 vaccine). There is an urgent need for policymakers to improve the health equity of people with dementia in the pandemic. Tackling COVID-19 inequities requires revisiting infection control policies to improve access to health and support services, recognizing the essential role of family care partners, and providing resources to help support people with dementia during the pandemic. Moreover, it is essential that COVID-19 policy responses are informed by evidence-informed research and authentic partnerships that embrace the insight and lived experiences of 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".