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Record W3175831536 · doi:10.25071/2291-5796.79

Career Advancement: The Experiences of Minority Nurses in Accessing Leadership Positions in a Tertiary Care Setting

2021· article· en· W3175831536 on OpenAlexaffvenueabout
Naima Bouabdillah, Amélie Perron, Dave Holmes

Bibliographic record

VenueWitness The Canadian Journal of Critical Nursing Discourse · 2021
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of OttawaUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsWorkforceNonprobability samplingNursingHealth careLeadership developmentFace (sociological concept)PsychologyEthnographyMedical educationMedicinePublic relationsPolitical scienceSociology

Abstract

fetched live from OpenAlex

Minority nurses are underrepresented in leadership roles in the Canadian healthcare system. The purpose of this study was to explore MNs’ perceptions and experiences with regards to career development and MNs in leadership positions. Twelve nurses, four Caucasian and eight from the Caribbean and Africa in a tertiary care setting were recruited through purposive sampling. Face-to-face semi-structured interviews were conducted, transcribed, coded, and analyzed using critical ethnography. Findings revealed lack of social support, of equal opportunities, of recognition and of trust. Despite negative experiences, minority nurses recognized the value of their work experience at the hospital where they were employed. Committing to a diverse workforce in leadership roles can ultimately have an effect on patient care. Minority nurses’ leadership is needed to provide role models and to ensure the delivery of competent care to diverse populations.

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)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.422

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0200.007
Scholarly communication0.0050.003
Open science0.0020.009
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.388
Teacher spread0.343 · 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

Citations12
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueWitness The Canadian Journal of Critical Nursing DiscourseSame topicMigration, Health and TraumaFrench-language works237,207