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Record W2164985673 · doi:10.12927/cjnl.2003.16254

Honours Education: Releasing Leadership Potential

2003· review· en· W2164985673 on OpenAlexaffvenueabout
Angela Gillis

Bibliographic record

VenueNursing leadership · 2003
Typereview
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsLeadership developmentCurriculumEducational leadershipNurse educationProfessional developmentNeuroleadershipNursingPolitical scienceLeadership studiesPedagogyMedical educationSociologyPsychologyLeadership styleMedicinePublic relations

Abstract

fetched live from OpenAlex

There is debate within the nursing profession in regard to determining the best approach to Leadership development for the new millennium. Should nursing adopt career pathways Like other disciplines that enable individuals to develop leadership potential in a timely fashion? St. Francis Xavier University (StFXU), the number one ranked undergraduate school in the country (DeMont 2002), has established an innovative strategy that promotes Leadership development at the undergraduate level. It has launched a special stream of its BScN program that culminates in an honours degree. The program, the first of its type in Canada, is designed to produce nursing leaders and scholars who will possess the core competencies required for leadership in diverse environments. This paper discusses the role of honours education in nursing, describes the curriculum and related Learning activities in the StFXU honours program and explores the benefits and challenges that an honours program has to offer. The findings will benefit nurse leaders in educational and practice settings, professional organizations and policy arenas who are interested in influencing the development of leadership in nursing.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.260
GPT teacher head0.386
Teacher spread0.126 · 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
GenreReview

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

Citations4
Published2003
Admission routes3
Has abstractyes

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