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Record W4396232072 · doi:10.12927/hcpol.2024.27281

Rural-Urban Differences in Healthcare Use in Persons With Dementia Between 2000 and 2019: A Quebec Population-Based Study

2024· article· en· W4396232072 on OpenAlexafffundvenueabout
Geneviève Arsenault‐Lapierre, Claire Godard‐Sebillotte, Tammy Bui, Nadia Sourial, Louis Rochette, Victoria Massamba, Caroline Sirois, Julie Kosteniuk, Debra Morgan, Amélie Quesnel‐Vallée, Isabelle Vedel

Bibliographic record

VenueHealthcare policy · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityCanadian Rural Health Research SocietyInstitut National de Santé Publique du QuébecCentre de Santé et de Services Sociaux CavendishJewish General HospitalUniversité LavalUniversité de MontréalMcGill University Health Centre
FundersCanadian Institutes of Health ResearchConsortium canadien en neurodégénérescence associée au vieillissementFP7 Research infrastructuresRéseau québécois de recherche sur le vieillissementMcGill University
KeywordsDementiaMedicineHealth servicesPopulationGerontologyDemographyGeographyEnvironmental health

Abstract

fetched live from OpenAlex

Background: Rural persons with dementia face medical services gaps. This study compares the health service utilization of rural and urban community-dwelling individuals with incident dementia. Methods: This study used a repeated annual cross-sectional cohort design spanning a period from 2000 to 2019 analyzing age-adjusted rates for 20 indicators of service use and mortality one year after diagnosis in Quebec administrative databases. Results: Of 237,259 persons, 20.1% were rural. Most rural persons had more emergency department visits and hospitalizations, shorter stays, less alternate level of care and fewer family physicians' and cognition specialists' visits. All groups had similar long-term care and mortality rates. Conclusion: Policy implications of these disparities 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.043
GPT teacher head0.377
Teacher spread0.334 · 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 designObservational
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

Citations6
Published2024
Admission routes4
Has abstractyes

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