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Record W4385993657 · doi:10.1016/j.lana.2023.100577

Promoting children’s rights to health and well-being in the United States

2023· review· en· W4385993657 on OpenAlexaff
Audrey R. Chapman, Luca Brunelli, Lisa Forman, Joseph W. Kaempf

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2023
Typereview
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsConvention on the Rights of the ChildHealth careLife expectancyEconomic growthConventionPer capitaPolitical scienceRight to healthHuman rightsMedicineLawEnvironmental healthEconomicsPopulation

Abstract

fetched live from OpenAlex

The United States has a highly sophisticated pediatric healthcare system and spends more than any other country per capita on children's healthcare. However, not all children have access to needed and affordable health care and the life expectancy and health outcomes of children in the country are worse than in any other industrialized nation. These nations typically offer universal healthcare for children as part of a robust recognition of a children's rights framework. In 1989 the United Nations adopted the Convention on the Rights of the Child that recognizes the right of the child to the highest attainable standard of health and to facilities for the treatment of illness and rehabilitation of health. Currently the United States is the only United Nations member country that has not ratified the Convention on the Rights of the Child. This paper outlines the potential benefits of adopting a child rights approach based on the principles and provisions of the Convention on the Rights of the Child. The fact that countries who invest much less in healthcare compared to the United States can achieve better health outcomes provides the certainty that a solution is possible and within reach.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.588
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.003
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
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 head0.183
GPT teacher head0.490
Teacher spread0.307 · 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
GenreReview

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

Citations13
Published2023
Admission routes1
Has abstractyes

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