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Record W4376850040 · doi:10.12927/cjnl.2023.27077

Smashing the “Black Ceiling”: The Black Nurses Leadership Institute

2023· article· en· W4376850040 on OpenAlexaffvenue
Shelly Philip LaForest, Saudia Jadunandan

Bibliographic record

VenueNursing leadership · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsRegistered Nurses' Association of OntarioBrampton Civic Hospital
Fundersnot available
KeywordsWhite (mutation)NursingGlass ceilingCeiling (cloud)Political sciencePsychologyMedical educationPublic relationsMedicineLawEngineering

Abstract

fetched live from OpenAlex

The Black Nurses Leadership Institute launched in May 2022 to provide a community-driven and leadership training program for nurses and nursing students who identify as Black and/or of African descent (Black Nurses Leadership Institute 2022). The aim of the program is to acknowledge and address the presence of a "black ceiling" that can often challenge and impede professional advancement for Black nurses in traditionally white-dominated healthcare leadership systems (Erskine et al. 2021; McGirt 2017). This collaborative experience creates a sense of belonging and offers a welcome space for learning among like-minded individuals with shared experiences.

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)0.010
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0250.009
Scholarly communication0.0080.006
Open science0.0010.020
Research integrity0.0030.015
Insufficient payload (model declined to judge)0.0130.002

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 head0.389
GPT teacher head0.389
Teacher spread0.000 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

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