The Brain Health Support Program: A web‐based interactive platform to increase dementia literacy and awareness regarding lifestyle factors in at‐risk individuals
Bibliographic record
Abstract
Abstract Background Prior studies have reported that older adults can benefit from formal educational programs about dementia. Participating in an online educational program focusing on risk and protective factors could potentially increase participants’ dementia literacy, empowerment, and engagement in brain health promotion, which in turn could reduce their dementia risk. The goal is to develop a comprehensive, compelling, and evidence‐based online educational program on risk and protective factors for dementia. Method The Brain Health Support Program is being developed as part of the Canadian Consortium on Neurodegeneration in Aging CAN‐Thumbs‐Up initiative. The program is designed to improve dementia literacy, promote lifestyle changes in at‐risk individuals who are cognitively intact or have mild cognitive impairment, and evaluate its effectiveness. Results The content is based on the epidemiological literature on risk and protective factors for dementia. The format is determined from a review of web‐based educational programs for older adults and is co‐created with experts, stakeholders and citizen advisors. The program contains eight interactive modules with new content provided weekly over a 12‐month period. The modules contain general information and tips on modifiable risk factors including diet, physical activity, cognitively stimulating activity, sleep, vascular health, social and psychological factors, vision and hearing, as well as information on dementia, stigma, and stereotypes. Participants have access to an individualized risk profile to determine personal goals and are given feedback on lifestyle changes. Content is available in French and English. Changes in dementia literacy, self‐efficacy, attitudes toward dementia and modifiable risk factors will be collected from the platform. Conclusion Providing access to scientifically validated education through an interactive web‐based platform is expected to have a positive effect on participants’ attitude, engagement in brain health behaviours and dementia literacy. It might also increase readiness to change and maintain positive lifestyle changes. The content and format are co‐created with users and stakeholders, which should increase its relevance and facilitate future implementation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".