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Record W1944391978 · doi:10.32316/hse/rhe.v15i2.456

Les Soeurs Grises à l’Université de Montréal, 1923-1947 : dela gestion hospitalière à l’enseignement supérieur en nursing

2003· article· en· W1944391978 on OpenAlexaffvenueabout
Yolande Cohen, Esther Lamontagne

Bibliographic record

VenueHistorical Studies in Education / Revue d histoire de l éducation · 2003
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsSecularizationDemocratizationNursingHealth careContinuancePolitical scienceSociologyDemocracyMedicineLawPolitics

Abstract

fetched live from OpenAlex

This article presents the role of the Grey Nuns in the development of the teaching of nursing in Quebec universities. The authors analyze the processes which have led to the formalization of this discipline through the examination of their archives and those of the Faculty of Nursing Science of the Université de Montréal. They reveal the transfer of practical knowledge that Grey Nuns have acquired as administrators in the major hospitals of the country to theoretical knowledge which is essential to the establishment of nursing as a science. In this manner, the Grey Nuns have also been involved in the process of secularization of health care while confirming their irrevocable tie to Catholic methodologies. Thus emerges a model of care as the structural element of hospital organization resisting the multiple transformations in the health care field in the period following the First World War. Furthermore, in relation to other works, the importance of this model is highlighted as well as its continuance in the educational and health care system in spite of the democratization and secularization movement in the 1970s.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.441

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.007
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.000

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 head0.053
GPT teacher head0.266
Teacher spread0.213 · 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
Published2003
Admission routes3
Has abstractyes

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