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Record W2155344799 · doi:10.1017/s0714980810000309

Aging and Health: An Examination of Differences between Older Aboriginal and non-Aboriginal People

2010· article· fr· W2155344799 on OpenAlexafffundabout
Kathi Wilson, Mark W. Rosenberg, Sylvia Abonyi, Robert Lovelace

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2010
Typearticle
Languagefr
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of SaskatchewanQueen's UniversityThe Wilson CentreFleming CollegeUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPopulationDemographyGerontologyMedicinePopulation ageingHealth carePopulation healthCommunity healthPublic healthEnvironmental healthSociologyPolitical science

Abstract

fetched live from OpenAlex

The Aboriginal population in Canada, much younger than the general population, has experienced a trend towards aging over the past decade. Using data from the 2001 Aboriginal Peoples Survey (APS) and the 2000/2001 Canadian Community Health Survey (CCHS), this article examines differences in health status and the determinants of health and health care use between the 55-and-older Aboriginal population and non-Aboriginal population. The results show that the older Aboriginal population is unhealthier than the non-Aboriginal population across all age groups; differences in health status, however, appear to converge as age increases. Among those aged 55 to 64, 7 per cent of the Aboriginal population report three or more chronic conditions compared with 2 per cent of the non-Aboriginal population. Yet, among those aged 75 and older, 51 per cent of the Aboriginal population report three or more chronic conditions in comparison with 23 per cent of the non-Aboriginal population.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.593
Threshold uncertainty score0.810

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.300
Teacher spread0.282 · 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

Citations40
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicHealth disparities and outcomesFrench-language works237,207