MétaCan
Menu
Back to cohort
Record W2922006403 · doi:10.12927/cjnl.2019.25756

Black Nurse Leaders in the Canadian Healthcare System

2018· article· en· W2922006403 on OpenAlexaffvenueabout
Keisha Jefferies, Megan Aston, Gail Tomblin Murphy

Bibliographic record

VenueNursing leadership · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsNursingWorkforceHealth careVisibilityNurse educationRepresentation (politics)MedicinePsychologyPoliticsPolitical science

Abstract

fetched live from OpenAlex

This article highlights a growing gap in the Canadian nursing workforce, specifically in nursing leadership. Black nurses are significantly underrepresented in nursing and even more so as nurse leaders. This commentary will provide a brief background related to Black nurses in healthcare, a description of nursing leadership, the significance of having Black nurses in leadership positions and finally how to move towards increasing the representation and visibility of Black nurse leaders. This commentary is timely and necessary, as it will describe how Black nurse leaders can enrich the nursing profession as well as the lives of Black individuals, families and communities.

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)0.005
metaresearch head score (Gemma)0.012
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: none
Teacher disagreement score0.897
Threshold uncertainty score0.748

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0350.005
Scholarly communication0.0050.002
Open science0.0020.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0150.001

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 head0.362
GPT teacher head0.462
Teacher spread0.100 · 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

Citations10
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueNursing leadershipSame topicGlobal Health Workforce IssuesFrench-language works237,207