MétaCan
Menu
Back to cohort
Record W2966414021 · doi:10.1177/0164027519864720

Does the Association Between Age and Major Illness Vary by Healthcare System Quality?

2019· article· en· W2966414021 on OpenAlexaff
Matthew A. Andersson, Lindsay R. Wilkinson, Markus H. Schafer

Bibliographic record

VenueResearch on Aging · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHealth careHealthcare systemMedicineQuality (philosophy)Test (biology)Association (psychology)GerontologyEnvironmental healthPsychology

Abstract

fetched live from OpenAlex

This study builds on research into global aging, by offering a multiple-indicator test of whether national healthcare system quality modifies the association between age and major illness. Recent individual-level data on morbidity among respondents aged 50 or older (16 countries; 2014 European Social Survey) are merged with nation-level healthcare indicators. Healthcare system quality is assessed using a subjective, evaluation-based approach and an objective, attributable-mortality approach. Lagged nation-level economic and health indicators are controlled to help isolate healthcare system effects. Results across subjective and objective approaches to healthcare system quality are strikingly consistent. While older individuals showed approximately a 10% reduction in probability of major illness when residing in countries with higher healthcare quality, associations between age and morbidity indices combining number and severity of illness showed greater modification by healthcare quality, with reductions around 18%. Taken together, results are suggestive of healthcare's protective role in reducing age-related illness and disability.

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)0.014
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, 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 score0.224
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.162
GPT teacher head0.561
Teacher spread0.399 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueResearch on AgingSame topicGlobal Health Care IssuesFrench-language works237,207