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Record W4383765066 · doi:10.56687/9781447344964-008

Rural dementia research in Canada

2020· book-chapter· en· W4383765066 on OpenAlexaboutno aff
Debra Morgan, Julie Kosteniuk, Megan E. O’Connell, Norma J. Stewart, Andrew Kirk

Bibliographic record

VenuePolicy Press eBooks · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaGeographyPsychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Although rural Canada makes up 95% of the country’s land mass, Canada is becoming increasingly urbanized as cities grow and the rural proportion has declined and aged. These changes have socio-economic impacts on rural communities, including ability to deliver health and social services for aging rural populations. The challenges of aging in rural communities, such as disparities in access to services, are compounded when living with dementia. This chapter reviews the Canadian dementia care context, issues and challenges in rural dementia care, and Canadian research addressing these issues. The chapter describes the Rural Dementia Action Research (RaDAR) Program based in the western Canadian province of Saskatchewan, which has been focused on rural dementia care for over 20 years. Key RaDAR projects include the development of an interdisciplinary specialist memory clinic serving rural and remote areas, and rural primary health care memory clinics to increase access to coordinated team-based care in rural communities.

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)0.005
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.103
Threshold uncertainty score0.745

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.010
Science and technology studies0.0160.004
Scholarly communication0.0070.001
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0310.002

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 head0.363
GPT teacher head0.487
Teacher spread0.124 · 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".

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Citations1
Published2020
Admission routes1
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