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Record W2332597473 · doi:10.1017/s0714980800014045

Prevalence, Risk Factors and Self-Reported Medical Causes of Seeing and Hearing-Related Disabilities Among Older Adults

2000· article· fr· W2332597473 on OpenAlexaffabout
Parminder Raina, Micheline Wong, Steven Dukeshire, Larry W. Chambers, Joan Lindsay

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2000
Typearticle
Languagefr
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsMcMaster UniversityHealth CanadaChildren’s Health Research InstituteUniversity of British Columbia
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

RÉSUMÉ On a examiné la prévalence, les facteurs de risque et les troubles médicaux associés aux déficiences de la vue et de l'ouïe chez les adultes canadiens de 55 ans et plus. On a établi un échantillonnage aléatoire à partir des recensements canadiens de 1986 et 1991 et on a demandé aux citoyens qui en faisaient partie de remplir le Sondage sur la santé et les limitations d'activité (ESLA) de 1986 et 1991. On a constaté que les aîné(e)s de 65 ans et plus présentaient plus de déficiences sensorielles que ceux de 55 à 64 ans. Les hommes signalaient plus de difficultés de l'ouîe que les femmes tandis que les femmes présentaient plus de difficultés de la vue que les hommes. On a constaté que l'incidence des difficultés sensorielles semblait augmenter avec l'âge et avec la diminution de revenu total de la maisonnée. Ce sont les cataractes et la surdité qui ont été le plus souvent mentionnées comme cause de restriction des activités de la vie quotidienne dans les deux groupes d'âge. Les difficultés sensorielles sont fréquentes chez les aîné(e)s. Les initiatives de santé publique devraient se pencher sur les difficultés de la vue et de l'ouïe, particulièrement chez les aîné(e)s, les femmes et les gens à faible revenu.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.255
Teacher spread0.244 · 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

Citations20
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicOphthalmology and Visual Impairment StudiesFrench-language works237,207