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Record W2331358375 · doi:10.1136/bmj.g7066

US elderly find it harder to pay medical bills than counterparts elsewhere, survey finds

2014· article· en· W2331358375 on OpenAlexaboutno aff
Michael McCarthy

Bibliographic record

VenueBMJ · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Services Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCommonwealthTelephone surveyFoundation (evidence)Health carePolitical scienceMedicineEconomic growthPublic administrationGerontologyBusinessEconomicsLawAdvertising

Abstract

fetched live from OpenAlex

US adults aged 65 years or over are more likely to have chronic health problems and to struggle to pay for medical care than are their counterparts in 10 other developed nations, a new survey indicates.1 However, the Americans surveyed were more likely to report that their doctors had discussed with them how to live a healthy life and to have had conversations with their doctor about advanced care planning and to have a written plan regarding the end of life care they desired. The survey, published by Health Affairs ,1 was conducted by the Commonwealth Fund, a foundation based in New York City that focuses on health system reform. The lead author was Robin Osborn, the fund’s vice president and director of international health policy and practice innovations. This was the 17th international survey sponsored by the foundation to compare different health systems. The study was based on a telephone survey of 15 617 adults aged 65 or older in Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the …

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 score0.061
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.090
GPT teacher head0.478
Teacher spread0.387 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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