Defy Dementia: Mobilizing a Public Health Awareness Campaign for Dementia Prevention
Bibliographic record
Abstract
Abstract Background While age is the most significant risk factor for dementia, increased awareness and understanding of other modifiable risk factors of dementia, coupled with proactive lifestyle behavior changes, hold the potential to prevent dementia and improve the quality of life for older adults. Defy Dementia is a public health initiative, led by the Baycrest Academy for Research and Education (BARE) and funded by the Public Health Agency of Canada. It involves curating, co‐designing, and disseminating a series of knowledge products to raise public awareness of dementia prevention and reduce stigma associated with dementia. These knowledge products, including podcasts, minute‐videos, and infographics, aim to raise awareness about modifiable risk factors associated with dementia and empower individuals to take proactive measures. Method To assess the impact of our knowledge products our evaluation consisted of two main components: a) quantitative data obtained through an online survey on the project website (convenience sample); and b) qualitative data obtained through 4 focus groups (each with a selected group of 4‐8 participants, derived from the first convenience sample and event participants). Quantitative data will be exported from REDCap and explored through descriptive statistics using SAS System version 9.4 or R version 4.2. Thematic analysis of the focus group data will be performed using NVivo R. Result Based on our hypothesis, we anticipate that individuals who engage with the knowledge products or attend an event will experience an improvement in their awareness and understanding of the modifiable risk factors associated with dementia, have enhanced knowledge about dementia prevention, exhibit behaviour change and have changed attitudes about PLWD. Conclusion We will demonstrate how meaningfully engaging older adults, persons living with dementia, and their care partners in the design and dissemination of knowledge can not only improve the relevance and uptake of the knowledge but also foster empathy and reduce stigma associated with dementia. We will also engage the audience to consider how increased awareness of modifiable risk factors for dementia, coupled with action, has the potential to lower the risk of developing dementia, ultimately leading to its prevention or delay and enhancing the quality of life for older adults.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".