MétaCan
Menu
Back to cohort
Record W4390192296 · doi:10.1002/alz.075156

Brain Health PRO: An Interactive, Online, Educational Program for Dementia

2023· article· en· W4390192296 on OpenAlexaff
Sylvie Belleville, Howard Chertkow, Howard Feldman, Haakon B. Nygaard, Manuel Montero‐Odasso, Nicole D. Anderson, Louis Bherer, Guylaine Ferland, Richard Camicioli, Senny Chan, Marc Cuesta, Emily Dwosh, Alexandra Fiocco, Brigitte Gilbert, Inbal Itzhak, Pamela Jarrett, Danielle Laurin, Teresa Liu‐Ambrose, Jody‐Lynn Lupo, Chris A. McGibbon, Laura E. Middleton, Kelly J. Murphy, Natalie Phillips, M. Kathleen Pichora‐Fuller, Carolyn Revta, Marie Y. Savundranayagam, Andrew Sexton, Eric E. Smith, Mark Speechley, Amal Trigui, Walter Wittich

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHotchkiss Brain InstituteUniversity of CalgarySimon Fraser UniversityConcordia UniversityUniversity of WaterlooToronto Metropolitan UniversityInstitut Universitaire de Gériatrie de MontréalHorizon Health NetworkUniversity of British ColumbiaQuebec Network for Research on AgingUniversity of British Columbia HospitalJewish General HospitalBaycrest HospitalUniversity of AlbertaUniversity of New BrunswickUniversité de MontréalWestern University
Fundersnot available
KeywordsDementiaUsabilityHealth literacyFocus groupPsychologyMedical educationSystem usability scaleIntervention (counseling)LiteracyCognitionApplied psychologyMedicineGerontologyWeb usabilityHealth careComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Abstract Background The Intervention mapping framework was used to plan and develop Brain Health PRO (BHPro), a 45‐week, theory‐ and evidence‐based online educational program co‐created with users in English and French. The goal of the program is to improve dementia literacy, self‐efficacy, and attitude toward dementia and ultimately have a positive impact on the dementia risk profile. A pilot study evaluated usability and acceptability using a shorter version of the online program, which informed changes for a full efficacy trial. Method A group of over 100 researchers contributed expert content targeting seven modifiable risk factors (physical activity, nutrition, cognitive engagement, sleep, social and psychological health, vascular health, vision and audition). 181, ten minute chapters were created in total. All chapters were reviewed and edited by the Citizen’s Advisory group, consisting of older adults in the community. Information and recommendations for lifestyle improvement are delivered incrementally through weekly email interactions, and the platform is designed based on e‐learning principles. Participants complete online questionnaires to assess usability (eg: ease of use, ease of navigation, access, complexity), acceptability (eg: whether the program is interesting, whether they would use it again or recommend it) and level of risk at baseline for each of the seven modifiable risk factors. Result A pilot study of Brain Health PRO was conducted, including 20 older adults, 13 women and 7 men. They had 16.75 years of education on average and their mean score on Logical Memory Scale II was 10.90. Two focus groups were conducted with a randomly selected group of participants to inform about the usability and acceptability of the program. The focus groups along with user experience questionnaires indicated excellent usability, with all dimensions rated in the positive range. Feedback from the end users were incorporated into the program prior to the main efficacy study. Conclusion BHPro was designed out of necessity to pivot in the changing environment brought by COVID‐19. Early data indicate that the BHPro program meets the needs and abilities of older adults in a virtual setting. The platform is easy for older adults to learn and use and is considered relevant.

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)0.002
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.064
GPT teacher head0.434
Teacher spread0.370 · 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
GenreOther

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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & DementiaSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207