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Record W3112607475 · doi:10.1002/alz.042603

The Brain Health Support Program: A web‐based interactive platform to increase dementia literacy and awareness regarding lifestyle factors in at‐risk individuals

2020· article· en· W3112607475 on OpenAlexaffabout
Sylvie Belleville, Haakon B. Nygaard, Louis Bherer, Richard Camicioli, Julie Carrier, Nicole D. Anderson, Thien Thanh Dang‐Vu, Emily Dwosh, Guylaine Ferland, Elaine Harris, Danielle Laurin, Teresa Liu‐Ambrose, Lisa Madlensky, Lesley Ellis Miller, Manuel Montero‐Odasso, Natalie Phillips, M. Kathleen Pichora‐Fuller, Julie M. Robillard, Eric E. Smith, Mark Speechley, Walter Wittich, Howard Chertkow, Howard Feldman

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of CalgaryParkwood InstituteVancouver Coastal HealthConcordia UniversityBaycrest HospitalWestern UniversityUniversity of TorontoUniversity of British ColumbiaQuebec Network for Research on AgingUniversity of British Columbia HospitalUniversité LavalUniversity of AlbertaUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsDementiaLiteracyHealth literacyPsychologyPromotion (chess)GerontologyMedicineHealth careDiseasePedagogy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.002

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.

Opus teacher head0.037
GPT teacher head0.361
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes2
Has abstractyes

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