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Record W148924748

Household spending on health care.

2000· article· en· W148924748 on OpenAlexaffabout
Robbi Chaplin, Lois Earl

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsHealth careHealth spendingEconomicsPer capitaLiberian dollarDemographic economicsInflation (cosmology)Consumer spendingDemographicsConsumer Expenditure SurveyHealth insurancePublic economicsDemographyAggregate expenditureMedicineEconomic growthPopulationEnvironmental healthFinance
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: This article examines changes in household spending on health care between 1978 and 1998. It also provides a detailed look at household spending on health care in 1998. DATA SOURCES: Data on household spending are from Statistics Canada's Family Expenditure Survey for survey years between 1978 and 1996, and from the annual Survey of Household Spending for 1997 and 1998. ANALYTICAL TECHNIQUES: Proportion of after-tax spending was calculated by subtracting average personal income taxes from average total expenditures and then dividing health care expenditures by this figure. Per capita spending was calculated by dividing average household spending by average household size. Constant dollar figures and adjustments for inflation were calculated using the Consumer Price Index (1998 = 100) to control for the effect of inflation over time. MAIN RESULTS: Almost every Canadian household (98.2%) reported health care expenditures in 1998, spending an average of close to $1,200, up from around $900 in 1978. In 1998, households dedicated a larger share of their average after-tax spending (2.9%) to health care than they did 20 years earlier (2.3%). Health insurance premiums claimed the largest share (29.8%) of average health care expenditures, followed by dental care, then prescription medications and pharmaceutical products.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.116
GPT teacher head0.413
Teacher spread0.297 · 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 designNot applicable
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

Citations17
Published2000
Admission routes2
Has abstractyes

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