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Record W2911744438 · doi:10.1080/24745332.2018.1483784

Respiratory medicine in Nunavut and Northern Canada

2019· article· en· W2911744438 on OpenAlexaffabout
Thomas Kovesi

Bibliographic record

VenueCanadian Journal of Respiratory Critical Care and Sleep Medicine · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsIndigenousTuberculosisMedicineEnvironmental healthPovertyHealth careIntervention (counseling)Economic growthNursingBiology

Abstract

fetched live from OpenAlex

Relatively little is known about Inuit health pre-colonization. Health has formed a major part of the history of colonization; both in terms of new exposures to infectious diseases — particularly respiratory pathogens — brought by Europeans, and in terms of Inuit and government perceived needs for health care. Tuberculosis (TB) has been a major cause of mortality for Inuit and northern First Nations (FN) individuals from the 1860s until the 1950s. The establishment of sanatoria distant from people's communities, case finding using patrol boats and forced relocation of infected individuals, and mandatory establishment of permanent communities — at least partly to provide health care — has been an important source of intergenerational trauma for Canadian indigenous peoples. Other important epidemics of respiratory pathogens have included influenza, pertussis, and respiratory syncytial virus. Post-infectious bronchiectasis is particularly common in Inuit youth, and lung cancer is a growing challenge for elders. While access to advanced diagnostic techniques in the North is improving, research needs to focus on optimal use of new diagnostic techniques and intervention studies. Ultimately, reducing respiratory morbidity and mortality will require major improvements in the social determinants of health, including poverty, education, access to clean water, nutrition and housing.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.344
Teacher spread0.312 · 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 designObservational
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

Citations2
Published2019
Admission routes2
Has abstractyes

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