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Record W4391132914 · doi:10.1123/japa.2022-0401

Dementia-Inclusive Choices for Exercise Toolkit: Impact on the Knowledge, Perspectives, and Practices of Exercise Providers

2024· article· en· W4391132914 on OpenAlexafffund
Laura E. Middleton, Chelsea Pelletier, Melissa Koch, Rebekah Norman, Sherry L. Dupuis, Arlene Astell, Lora Giangregorio, Shannon Freeman

Bibliographic record

VenueJournal of Aging and Physical Activity · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Northern British ColumbiaResearch Institute for AgingUniversity of Waterloo
FundersCanadian Institutes of Health ResearchAlzheimer SocietyConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsDementiaThematic analysisDicePsychologyGerontologyMedicineQualitative researchDiseaseSociology

Abstract

fetched live from OpenAlex

Physical activity improves the well-being of persons living with dementia but few exercise programs include them. The Dementia-Inclusive Choices for Exercise (DICE) toolkit aims to improve exercise providers' understanding of dementia and ability to support persons living with dementia in physical activity. We evaluated the co-designed DICE toolkit with exercise providers using a mixed-methods approach comprising pre/post questionnaires and interviews and reflection diaries. Among 16 participants, self-efficacy for exercise delivery to persons living with dementia and both knowledge and attitudes toward dementia significantly improved. Thematic analysis suggested participants (a) had a deeper understanding of the variability of dementia, (b) were planning for equitable access for persons living with dementia, (c) planned to promote social connection through exercise, and (d) were optimistic for future engagement with persons living with dementia. The DICE toolkit may improve exercise providers' knowledge and confidence to plan proactively to support persons living with dementia in programs and services.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.401
Teacher spread0.376 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2024
Admission routes2
Has abstractyes

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