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Record W2154321119 · doi:10.12927/cjnl.2011.22141

The "Old Internationals": Canadian Nurses in an International Nursing Community

2010· article· en· W2154321119 on OpenAlexaffvenueabout
Jaime Lapeyre, Sioban Nelson

Bibliographic record

VenueNursing leadership · 2010
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLeagueNursingProfessionalizationNurse educationInternationalism (politics)Public healthPandemicMedicinePolitical sciencePoliticsCoronavirus disease 2019 (COVID-19)Law

Abstract

fetched live from OpenAlex

The vast devastation caused by both the First World War and the influenza pandemic of 1918 led to an increased worldwide demand for public health nurses. In response to this demand, a number of new public health training programs for nurses were started at both national and international levels. At the international level, one of two influential programs in this area included a year-long public health nursing course offered by the League of Red Cross Societies, in conjunction with Bedford College in London, England. In total, 341 nurses from 49 different countries have been documented as participants in this initiative throughout the interwar period, including 20 Canadians. Using archival material from the Canadian Nurses Association and the Royal College of Nursing, as well as articles from the journals Canadian Nurse, American Journal of Nursing and British Journal of Nursing, this paper examines these nurses' commitment to internationalism throughout their careers and explores the effect of this commitment on the development of nursing education and professionalization at the national level.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.510

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0580.016
Scholarly communication0.0110.004
Open science0.0030.011
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.000

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 head0.238
GPT teacher head0.407
Teacher spread0.169 · 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 designQualitative
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

Citations3
Published2010
Admission routes3
Has abstractyes

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