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Health Disparities: Indigenous: Native American

2022· other· en· W4298000883 on OpenAlexaboutno aff
Ashley Cordes

Bibliographic record

VenueThe International Encyclopedia of Health Communication · 2022
Typeother
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousHealth equityColonialismLife expectancyPovertyPolitical scienceMisinformationMetisHealth careEconomic growthMedicineEnvironmental health

Abstract

fetched live from OpenAlex

This entry offers an overview of Indigenous/Native American (sometimes referred to as American Indian/Alaska Native) health disparities and their ramifications through a communication lens. First is an elaboration of how Indigenous peoples experience disproportionate disease and lower life expectancy than other Americans. This is connected to colonialism and the unique status that Indigenous nations and urban Indigenous peoples hold in what is now the United States. Then the entry details systemic issues between settler entities and Indigenous communities that foment disparities in physical and mental health and access to care. Communication scholars have noted how colonially imposed poverty, digital divides, medical statistical misinformation, and culturally ineffective health messaging contribute to the problem. While disparities can be devastating, Indigenous peoples are actively engaged in reformation processes to demand change and contribute to a vision of health equity.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.058
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0030.001
Scholarly communication0.0030.003
Open science0.0000.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0580.007

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 head0.430
Teacher spread0.396 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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