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Record W834656760

Nunavut: A Health System Profile

2014· article· en· W834656760 on OpenAlexvenueaboutno aff
Karen Doty-Sweetnam

Bibliographic record

VenueCanadian journal of native studies · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)AppropriationHealth carePublic healthLibrary scienceSociologyPolitical sciencePublic administrationArchaeologyGeographyMedicineLawNursing
DOInot available

Abstract

fetched live from OpenAlex

Gregory P. Marchildon and Renee Torgerson, Nunavut: A Health System Profile. Montreal: McGill-Queens University Press, 2013.178 pages. ISBN 978-0-7735-4148-1. $29.95 paperback.On April 1,1999, against a backdrop of snow, ice, Northern lights and Southern dominance, Canada's newest territory, Nunavut was formed. In their own land the Inuit people could now begin the decolonization process from White appropriation and establish a governance model that would help to secure their land, culture and beliefs for future generations. Part of this model is the complex healthcare system which Gregory Marchildon and Renee Torgerson chose to research.Marchildon is a known expert in health systems. He is a professor in the Graduate School of Public Policy at the University of Regina and also holds a Canada Research Chair in Public Policy and Economic History. Renee Torgerson is a healthcare researcher with extensive experience in both academic and applied health services. In 2009 and the early part of 2010, the authors travelled to ''almost all of Nunavut's 24 hamlets and held key informant interviews with department staff and front-line providers, as well as, conducted an extensive inventory of services (x). The knowledge collected during this timeframe provided the base elements of study under review.The methodology used to framework the study was adapted to the Canadian context, the approach bears some resemblance to the template used in health-system-in-transition (HiT) country studies published by the WHO [World Health Organization] Regional Office for Europe on behalf of the European Observation on Health Systems and Policies (ix-x). In addition to unpacking the current healthcare system of delivery, the study presents key information about the health status of the people of Nunavut and the factors that explain why the territory has the costliest healthcare system in Canada.The research from this book provides a comprehensive study of the Nunavut health system, from its challenges to its triumphs since the repatriation of the land and governance back to the Inuit people ten years ago.Some of the statistics captured in the data around social determinants of health and health outcomes are staggering and are key elements to take into consideration for healthcare planning. For example, Nunavut has the youngest population of any territory or province and over 50 percent of Nunavut's residents are under 25 years of age, but almost 75 percent of the working-age Inuit population is unqualified for readily available employment because of lack of education (13,14). Overcrowding and poor housing have greatly impacted the overall health of Inuit children: they suffer from extremely high rates of illness, hospitalization, and death due to respiratory illnesses (21). And sadly, given that Nunavut has a suicide rate that is amongst the highest in the world (110); suicide has become an area of intensive study by various external researchers (110).The first chapter explores Inuit health in comparison to the Northwest Territories, Yukon and the Canadian average. Chapter two discusses the organizational structures of the current health system, while the third chapter discusses health benefits, funding and expenditures and the fourth, the unique health infrastructure which provides services over a large land base with no accessible roads from the north to the south or between communities. …

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)0.002
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.057
GPT teacher head0.325
Teacher spread0.268 · 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
Published2014
Admission routes2
Has abstractyes

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