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Record W3029981565

The Cost of Staying Alive: Healthcare in America

2020· article· en· W3029981565 on OpenAlexaboutno aff
Blake Benson

Bibliographic record

VenueValpoScholar (Valparaiso University) · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careBusinessComputer sciencePublic relationsPolitical scienceEconomicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

[Abstract] In America today, many go without proper healthcare treatment due to rising healthcare costs. In this essay, I argue that corporate America and the politicians they own are not only igniting rising healthcare costs but are also reaping the benefits, at the literal expense of American lives. In today’s political realm, the topic of healthcare comes up often with different perspectives. However, oftentimes a whole story is not told with regards to cost, who’s affected, and solutions that could be used to work on solving this crisis in America. In this paper, I argue that the costs of healthcare are too high due to administrative costs, costs of pharmaceuticals, and the corruption that exists on the federal level which allows both of these issues to plague America. In order to remedy these issues, I argue for a single-payer healthcare system. This system would work to decrease healthcare costs, provide coverage for more Americans, and save the American Government money in the long-run. While most critique this idea to solve the health crisis in America, stating it will be too expensive and offer little change. However, these have been unfounded in both studies and cases that have single-payer systems around the world. In this essay, I often reference Canada not only due to their close proximity but because they used to face these same healthcare issues America faced in the 1960’s. This paper hopes to better explain the rising costs of healthcare, how they affect Americans, and why it is so important we deal with the issue before costs become too high.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.080
GPT teacher head0.250
Teacher spread0.170 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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