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Record W1503142795

Describing Disability among High and Low Income Status Older Adults in Canada

2000· article· en· W1503142795 on OpenAlexaffabout
Parminder Raina, Micheline Wong, Larry W. Chambers, Margaret Denton, Amiram Gafni

Bibliographic record

VenueSocial and Economic Dimensions of an Aging Population Research Papers · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineGerontologyPopulationRheumatismLow incomePublic healthEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the prevalence, types, and severity of disabilities, as well as the medical conditions that may have caused disabilities among non-institutionalized older adults by high and low income. Disabled individuals aged 55 years and older were identified from the 1986 and 1991 Health and Activity Limitation Surveys. The overall unweighted sample sizes for each survey were 132, 337 in 1986 and 91, 355 in 1991. Approximately 40% of senior men and women reported having at least one disability, with women just slightly more likely than men to report being disabled. Almost twice as many senior women had low income compared with senior men. Mobility and agility disabilities were the most common types of disabilities reported by older adults. Arthritis/rheumatism was the medical condition most often reported as the primary cause of a disability among women. Men most often reported diseases of the ear and mastoid processes, with differences reported by low and high income respondents. Among 55-64 year olds, low income respondents were generally less likely to be categorized as mildly disabled and more likely to be categorized as severely disabled compared with high income respondents. In an effort to postpone or prevent disabilities in an ever-growing older population, public health initiatives are required to educate older adults about medical conditions and impairments that often lead to disability, particularly among low income seniors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.036
GPT teacher head0.337
Teacher spread0.301 · 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

Citations5
Published2000
Admission routes2
Has abstractyes

Explore more

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