MétaCan
Menu
← Back to cohort
Record W2734312943 · doi:10.1093/geroni/igx004.2642

MULTIPLE CHRONIC CONDITIONS IN RELATION TO DISABILITY AND SOCIAL PARTICIPATION: DATA FROM THE CLSA

2017· article· en· W2734312943 on OpenAlexaffabout
Lauren E. Griffith, Anne Gilsing, Edwin R. van den Heuvel, S. Nazmul, Philip D. St. John, Parminder Raina

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversity of ManitobaMcMaster University
Fundersnot available
KeywordsGerontologyDemographySocial engagementChronic diseasePopulationMultiple Chronic ConditionsPsychologyDiseaseMedicineEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

While much is known about the effect of individual chronic conditions (CCs) on people’s ability to undertake their everyday activities, less is known about effect of having multiple CCs. We will present data from over 20,000 Canadian men and women on population patterns of self-reported CCs and how different combinations of CCs impact disability and social participation. Preliminary data suggest that although the proportion of people with 2+ CCs increases with age (22% in 45–54 vs. 52% in 75–89 year olds) and tends to be higher in females than males (36% vs. 30%), the difference between genders narrows with age. As well, combinations of chronic conditions with the same disease count differentially impact activities of daily living and social participation in men compared to women, and in middle-aged compared to older adults. Understanding these differences could help to increase the efficiency and quality of clinical care and improve public health.

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)0.004
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 score0.981
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.011
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.158
GPT teacher head0.426
Teacher spread0.268 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueInnovation in Aging→Same topicChronic Disease Management Strategies→French-language works237,207→