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Record W2171241298 · doi:10.1377/hlthaff.2014.0947

International Survey Of Older Adults Finds Shortcomings In Access, Coordination, And Patient-Centered Care

2014· article· en· W2171241298 on OpenAlexaboutno aff
Robin Osborn, Donald Moulds, David Squires, Michelle M. Doty, Chloé Anderson

Bibliographic record

VenueHealth Affairs · 2014
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTelephone surveyHealth careDeveloped countryGerontologyFamily medicineChronic diseaseEconomic growthEnvironmental healthPopulationBusiness

Abstract

fetched live from OpenAlex

Industrialized nations face the common challenge of caring for aging populations, with rising rates of chronic disease and disability. Our 2014 computer-assisted telephone survey of the health and care experiences among 15,617 adults age sixty-five or older in Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States has found that US older adults were sicker than their counterparts abroad. Out-of-pocket expenses posed greater problems in the United States than elsewhere. Accessing primary care and avoiding the emergency department tended to be more difficult in the United States, Canada, and Sweden than in other surveyed countries. One-fifth or more of older adults reported receiving uncoordinated care in all countries except France. US respondents were among the most likely to have discussed health-promoting behaviors with a clinician, to have a chronic care plan tailored to their daily life, and to have engaged in end-of-life care planning. Finally, in half of the countries, one-fifth or more of chronically ill adults were caregivers themselves.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.068
GPT teacher head0.404
Teacher spread0.336 · 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

Citations220
Published2014
Admission routes1
Has abstractyes

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