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Record W4313566234 · doi:10.51542/ijscia.v3i6.15

Policy Issues Affecting the Health of Older Individuals in the United States

2022· article· en· W4313566234 on OpenAlexaff
Oyintoun-emi Ozobokeme, Okelue E Okobi, Uduak A. Udo, Maureen G. Boms, Adeyemi Adeosun, Jovita Koko, Chukwuebuka Agu, Ijeoma Christia Izundu, Emmanuel O. Egberuare, Oluwarotimi A. Ogunlami

Bibliographic record

VenueInternational Journal Of Scientific Advances · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMarkham Stouffville HospitalAlberta Health Services
Fundersnot available
KeywordsHealth careBusinessInstitutionalisationSocioeconomic statusAffect (linguistics)Economic growthLong-term carePopulationPopulation ageingUnit (ring theory)Public economicsMedicineEnvironmental healthEconomicsNursingPsychology

Abstract

fetched live from OpenAlex

The aging population is growing, in the United States and the rest of the world. Developed nations are now facing challenges with providing for this increasing unit of population. Aged adults account for a significant part of individuals utilizing health care services in these countries. As a result of this growth, long-term policies in the healthcare of the elderly need to be reviewed. This manuscript offers insight into the policies and the several elements that affect the provision of healthcare to the aging populace. Some of the issues addressed include medical care needs versus supply, alternatives to institutionalization, alternative delivery systems, financing long-term care services, the role of informal support systems, housing, and income maintenance. The demand for use of long-term care services is also significantly growing. Socioeconomic status and health behaviors throughout life affect the need for these services. However, financing and access to these services have become a concern for governments, and papers like this offer relevant data which can be used to reform and make informed policies.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.438
Teacher spread0.403 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

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