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Record W2587127176 · doi:10.3917/rsi.127.0071

Infirmières issues de minorités visibles et mobilité verticale en milieu hospitalier

2017· article· fr· W2587127176 on OpenAlexaffabout
Naima Bouabdillah, Dave Holmes, Jocelyne Tourigny

Bibliographic record

VenueRecherche en soins infirmiers · 2017
Typearticle
Languagefr
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Canada has experienced a significant change in its ethnic and cultural composition in recent decades. The sustained immigration from non-European countries has given rise to new generation of visible minorities. This new trend is clearly reflected in healthcare institutions. However, the number of visible minority nurses (VMN) is particularly low in management positions. This research adopting critical ethnography and postcolonial approach focuses on the career paths of VMN in Canadian healthcare institutions. Nurses (n = eight, MVN) and managers (n = four caucasian) participated in a series of semi-structured interviews to gather relevant information about the representativeness of the VMN in management positions. Theoretical framework « Othering » was used to guide this research as it makes the link with “la lutte de classement” of Bourdieu. Four main themes closely associated with barriers emerged from the analysis namely ; Hiring and Promotion ; instrumentalization of IMV ; interpersonal and suffering and defensive strategies. Results showed that the VMN faced obstacles, often invisible, that contribute to keeping them at a lower level of the institutional hierarchy, including the hiring and promotion process that they describe as unfair and discriminatory.

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)0.014
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0030.003

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 head0.220
GPT teacher head0.543
Teacher spread0.323 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2017
Admission routes2
Has abstractyes

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