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Record W2060715020 · doi:10.3402/ijch.v72i0.21402

Circumpolar Inuit health systems

2013· review· ru· W2060715020 on OpenAlexaffabout
Leanna Ellsworth, Annmaree O’Keeffe

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2013
Typereview
Languageru
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsInuit Circumpolar Council
Fundersnot available
KeywordsCircumpolar starLife expectancyIndigenousArcticGeographyInfant mortalityHealth careEconomic growthSocioeconomicsEnvironmental healthDeveloping countryMedicinePopulationEcologySociologyEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: The Inuit are an indigenous people totalling about 160,000 and living in 4 countries across the Arctic - Canada, Greenland, USA (Alaska) and Russia (Chukotka). In essence, they are one people living in 4 countries. Although there have been significant improvements in Inuit health and survival over the past 50 years, stark differences persist between the key health indicators for Inuit and those of the national populations in the United States, Canada and Russia and between Greenland and Denmark. On average, life expectancy in all 4 countries is lower for Inuit. Infant mortality rates are also markedly different with up to 3 times more infant deaths than the broader national average. Underlying these statistical differences are a range of health, social, economic and environmental factors which have affected Inuit health outcomes. Although the health challenges confronting the Inuit are in many cases similar across the Arctic, the responses to these challenges vary in accordance with the types of health systems in place in each of the 4 countries. Each of the 4 countries has a different health care system with varying degrees of accessibility and affordability for Inuit living in urban, rural and remote areas. OBJECTIVE: To describe funding and governance arrangements for health services to Inuit in Canada, Greenland, USA (Alaska) and Russia (Chukotka) and to determine if a particular national system leads to better outcomes than any of the other 3 systems. STUDY DESIGN: Literature review. RESULTS: It was not possible to draw linkages between the different characteristics of the respective health systems, the corresponding financial investment and the systems' effectiveness in adequately serving Inuit health needs for several reasons including the very limited and inadequate collection of Inuit-specific health data by Canada, Alaska and Russia; and second, the data that are available do not necessarily provide a feasible point of comparison in terms of methodology and timing of the available data collection. CONCLUSIONS: Despite the variations in the health systems as well as national, political and economic approaches, none is adequately addressing Inuit health needs. All Inuit populations still have significant gaps between their health status and those of broader national populations. Meaningful measurement and evaluation of the effectiveness of the respective health systems is severely hindered by the lack of relevant, Inuit-specific health data. The inadequacy, and in a number of cases absence of relevant data, hinders the design and development of a better and potentially more effective approach to delivering health services to Inuit.

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.242
Threshold uncertainty score0.481

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.008
Science and technology studies0.0030.001
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.088
GPT teacher head0.448
Teacher spread0.360 · 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
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

Citations24
Published2013
Admission routes2
Has abstractyes

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