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Record W2188507200

Trends in out-of-pocket health care expenditures in Canada, by household income, 1997 to 2009.

2014· article· en· W2188507200 on OpenAlexaffabout
Claudia Sanmartin, Deirdre Hennessy, Yuqian Lu, Michael R. Law

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of British ColumbiaStatistics Canada
Fundersnot available
KeywordsHousehold incomeHealth careAdjusted gross incomeTotal personal incomeLogistic regressionLow incomeGross incomeEconomicsSocioeconomicsDemographic economicsGeographyMedicinePublic economicsState income taxEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Canadian households are spending an increasing share of their household income on health care not covered by public plans. This study investigates trends in out-of-pocket expenditures for health care services and products by household income quintile from 1997 to 2009. DATA AND METHODS: Biennial estimates from the Survey of Household Spending between 1997 and 2009 were used to examine changes in out-of-pocket health care expenditures, by household income quintile. The statistical significance of these changes was assessed using linear and logistic regression. RESULTS: In 2009, the percentage of after-tax household income spent on health care among low-income households (5.7%) was nearly twice that of high-income households (2.6%). Approximately 40% of households in the two lowest income quintiles spent more than 5% of their total after-tax income on health care services and products, compared with 14% of households in the highest income quintile. The increase in spending between 1997 and 2009 was greatest for households in the lowest income quintile (63%). INTERPRETATION: Out-of-pocket health care expenditures have increased for households in all income quintiles, but the relative increase was greatest among households in lower income quintiles.

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 score0.373
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.032
GPT teacher head0.225
Teacher spread0.194 · 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

Citations35
Published2014
Admission routes2
Has abstractyes

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