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Record W2013382344 · doi:10.12927/hcpol.2009.20937

Aging in Atlantic Canada: Service-Rich and Service-Poor Communities

2009· article· en· W2013382344 on OpenAlexaffvenueabout
Jamie Davenport, Thomas Rathwell, Mark W. Rosenberg

Bibliographic record

VenueHealthcare policy · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsThe Scarborough Hospital
Fundersnot available
KeywordsService (business)SociologyPeer reviewHealth carePolitical sciencePublic relationsMedia studiesLibrary scienceLawBusiness

Abstract

fetched live from OpenAlex

The delivery of services for seniors in Canada is increasingly complex and challenging. Communities across Canada age at different rates, and the forces underlying the differences, such as "aging in place" and migration, vary from community to community. We have identified two types of aging communities: service-rich communities, in which seniors have good health status and better amenities, and service-poor communities, in which seniors have poor health status and limited amenities. We also report on results for Atlantic Canada from a national study of service provisions. Three issues stand out: (a) the impact on communities of migration and aging in place, (b) the factors that distinguish service-rich and service-poor communities and (c) the conditions necessary to create a service-rich community. All levels of government in Atlantic Canada must work together to develop policies and programs that create and sustain service-rich 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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0120.003
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.328
Teacher spread0.296 · 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 designQualitative
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

Citations9
Published2009
Admission routes3
Has abstractyes

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