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Record W7036023143

Arthritis in the Canadian Aboriginal Population: North-South Differences in Prevalence and Correlates

2011· article· en· W7036023143 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2011
Typearticle
Languageen
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsArthritisPublic healthRheumatismHealth careEpidemiologyPrevalence
DOInot available

Abstract

fetched live from OpenAlex

BackgroundInformation on arthritis and other musculoskeletal disorders among Aboriginal people is sparse. Survey data show that arthritis and rheumatism are among the most commonly reported chronic conditions and their prevalence is higher than among non-Aboriginal people.ObjectiveTo describe the burden of arthritis among Aboriginal people in northern Canada and demonstrate the public health significance and social impact of the disease.MethodsUsing cross-sectional data from more than 29 000 Aboriginal people aged 15 years and over who participated in the Aboriginal Peoples Survey 2006, we assessed regional differences in the prevalence of arthritis and its association with other risk factors, co-morbidity and health care use.ResultsThe prevalence of arthritis in the three northern territories (“North”) is 12.7% compared to 20.1% in the provinces (“South”) and is higher among females than males in both the North and South. The prevalence among Inuit is lower than among other Aboriginal groups. Individuals with arthritis are more likely to smoke, be obese, have concurrent chronic diseases, and are less likely to be employed. Aboriginal people with arthritis utilized the health care system more often than those without the disease.Conclusion: Aboriginal-specific findings on arthritis and other chronic diseases as well as recognition of regional differences between North and South will enhance program planning and help identify new priorities in health promotion.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.000
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 head0.180
GPT teacher head0.451
Teacher spread0.271 · 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.

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

Citations0
Published2011
Admission routes1
Has abstractyes

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