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Record W2917180527 · doi:10.2478/pcssr-2018-0026

Serving the Health Care and Leisure Needs of Ethnic Aged in Canada: Implications and Concerns

2018· article· en· W2917180527 on OpenAlexaffabout
George Karlis, Aida Stratas, Marianna Locke, François Gravelle, Genie Arora

Bibliographic record

VenuePhysical Culture and Sport Studies and Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsEthnic groupImmigrationHealth careDiversity (politics)Service providerGerontologyService (business)Face (sociological concept)Economic growthMedicineBusinessPolitical scienceSociologyMarketingSocial science

Abstract

fetched live from OpenAlex

Abstract Health care and leisure services, although different, are similar from the perspective that both focus on enhancing quality of life by improving health and wellbeing. Although both of these services are vitally important, some groups such as aged immigrants face a number of barriers that may limit their access to these services. This paper examines and discusses two related areas of the service sector – health care and leisure – and the growing concern to address the needs of Canada’s aging population, specifically, aged immigrants. The paper concludes with the following five suggestions for health care and leisure service providers to alleviate barriers faced by Canada’s ethnic aged: 1) Recognize that health care and leisure are closely related, 2) Understand the changing nature of society including trends in immigration, 3) Get to know society’s diversity of aged immigrants, 4) Evaluate current services provided, and 5) Establish future goals and directions.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.384

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0170.004
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.160
GPT teacher head0.495
Teacher spread0.335 · 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 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

Citations7
Published2018
Admission routes2
Has abstractyes

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