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Record W2111324293 · doi:10.5430/jha.v3n5p182

United States physician communication on cost of cancer care under the affordable health care act

2014· article· en· W2111324293 on OpenAlexvenueno aff
Laura Tenner, Aaron E. Carroll, Paul R. Helft

Bibliographic record

VenueJournal of Hospital Administration · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFamily medicineHealth careHealth care costCancerNursing

Abstract

fetched live from OpenAlex

Background: The aim of this study is to survey United States oncologists as healthcare system changes are implemented to reassess physician perceptions about the cost of cancer care and physicians’ perceived needs.Methods: From June through August of 2013, an electronic survey was sent to practicing oncologists across 50 states.Results: The electronic survey response rate was 15% (136 oncologists out of 899 total physicians) with respondents from 35 of the 50 states. Sixty percent of respondents thought that both out-of-pocket costs and healthcare system costs of cancer treatments were likely or extremely likely to have a larger effect on their decisions regarding which cancer treatments to recommend to patients in the future under the Affordable Care Act (ACA). A large majority of respondents felt that physician education was needed on the use of cost-effectiveness data and on communicating cost of therapies with patients, 91% and 85%, respectively.Conclusion: Respondents reported that their clinical treatment decisions are influenced by concerns over out-of-pocket patient costs, and that they want more cost and comparative effectiveness research as well as more education on how to communicate with patients about cost of therapy.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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 designTheoretical or conceptual
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

Citations0
Published2014
Admission routes1
Has abstractyes

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