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Record W1575725284 · doi:10.3138/cbmh.27.1.139

Indian Hospitals and Aboriginal Nurses: Canada and Alaska

2010· article· en· W1575725284 on OpenAlexaffvenueabout
Laurie Meijer Drees

Bibliographic record

VenueCanadian Journal of Health History · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsNursingHealth careHealth servicesService (business)MedicineDevolution (biology)Political sciencePopulationEnvironmental healthBusiness

Abstract

fetched live from OpenAlex

Between 1945 and the early 1970s, both Indian Health Services in Canada (IHS), and the Alaska Native Health Service (ANS) initiated programs and activities aimed at recruiting and training nurses/nurses aides from Canadian and Alaskan Native communities. In Alaska, the Mt. Edgecumbe Hospital in Sitka acted as a training facility for Alaska Native nurses' aides, while in Canada, the Charles Camsell Hospital served a similar function. These initiatives occurred prior to the devolution of health care to Aboriginal communities. The histories of these two hospitals provide a comparative opportunity to reveal themes related to the history of Aboriginal nurse training and Aboriginal health policies in the north. The paper outlines the structure and function of two main hospitals within the Indian Health and Alaska Native Health Services, discusses the historic training, and role of Aboriginal nurses and caregivers within those systems using both archival and oral history sources.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.061
Threshold uncertainty score0.445

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0260.010
Scholarly communication0.0070.002
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.262
Teacher spread0.257 · 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 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

Citations5
Published2010
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Health HistorySame topicIndigenous Health, Education, and RightsFrench-language works237,207