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Record W2884071250 · doi:10.1177/0733464818790190

Cognitive Health Promotion Program for Community-Dwelling Seniors: Who Are We Reaching?

2018· article· en· W2884071250 on OpenAlexafffundabout
Agathe Lorthios-Guilledroit, Manon Parisien, Kareen Nour, Baptiste Fournier, Danielle Guay, Nathalie Bier

Bibliographic record

VenueJournal of Applied Gerontology · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsUniversité de MontréalCentre intégré de santé et de services sociaux de Chaudière-AppalachesCentre Intégré de Santé et de Services Sociaux des LaurentidesSanté Montérégie
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsGerontologyCognitionHealth promotionPsychologyDementiaMedicineNursingPublic healthPsychiatryDisease

Abstract

fetched live from OpenAlex

This exploratory study examines the reach of Jog Your Mind, a multifactorial community-based program promoting cognitive vitality among seniors with no known cognitive impairment. The aim was to determine whether the program successfully reached its target population and to compare the characteristics of participants (sociodemographic, health, lifestyle, attitudes, and cognitive profile) with the general population of seniors. Twenty-three community organizations recruited 294 community-dwelling seniors willing to participate in the program. Descriptive analyses revealed that the participants were mostly Canadian-born educated women living alone. Participants' health profile and lifestyle behaviors were fairly similar to those of seniors in Québec and Canada. A large proportion of the participants were concerned about their memory. These results suggest that the program did not attract many hard-to-reach members of the population and reached seniors who may have had some cognitive challenges. Cues to action for improving the reach of cognitive health promotion programs are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.195
GPT teacher head0.516
Teacher spread0.321 · 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 teacher head, not a consensus.

Study designOther design
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

Citations4
Published2018
Admission routes3
Has abstractyes

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