MétaCan
Menu
Back to cohort
Record W1983328966 · doi:10.12927/hcq.2011.22485

Chronic Conditions More Than Age Drive Health System Use in Canadian Seniors

2011· article· en· W1983328966 on OpenAlexaffabout
Michael Terner, Ben Reason, A Moses McKeag, Brenda Tipper, Greg Webster

Bibliographic record

VenueHealthcare Quarterly · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCanadian Institute for Health Information
Fundersnot available
KeywordsMedical prescriptionHealth careMedicineChronic conditionFamily medicineHealth administrationChronic diseaseGerontologyPublic healthNursingDisease

Abstract

fetched live from OpenAlex

Which has more impact on health status and the use of healthcare services among seniors: age or the number of chronic conditions? To answer this question, we used responses from the 2008 Canadian Survey of Experiences with Primary Health Care to assess the effect of these two factors on seniors' self-perceived health status, prescription medication use and healthcare service use. We discovered that seniors with at least three chronic conditions were more likely to report poor health, take more prescription medications and use more healthcare services than seniors with two or fewer chronic conditions. The number of chronic conditions is better than age as a predictor of self-reported health status, prescription medication use and healthcare service use by seniors. Seniors with at least three conditions represented 24% of all seniors, but they accounted for 40% of the use of healthcare services. Health policies and programs focused on the prevention and improved management of co-morbidities among seniors could have a significant and positive impact on seniors' health (including self-perceived health status) and their use of healthcare services.

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)0.007
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.253
GPT teacher head0.396
Teacher spread0.143 · 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

Citations39
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueHealthcare QuarterlySame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207