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Record W2919461118 · doi:10.12927/cjnl.2019.25760

Building Healthcare Leadership Capacity: Strategy, Insights and Reflections

2018· article· en· W2919461118 on OpenAlexaffvenueabout
Julia Scott, Beverley Simpson, Judith Skelton‐Green, Sue Munro

Bibliographic record

VenueNursing leadership · 2018
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsInstitute for Work & Health
Fundersnot available
KeywordsLeadership developmentHealth careContext (archaeology)Leadership studiesPublic relationsShared leadershipNeuroleadershipPsychologyHealth professionalsEducational leadershipSelection (genetic algorithm)Transactional leadershipLeadership styleEngineering ethicsSociologyKnowledge managementPolitical sciencePedagogyComputer scienceEngineering

Abstract

fetched live from OpenAlex

The Dorothy Wylie Health Leaders Institute is a Canadian success story, providing leadership development to over 2,600 nurses and healthcare professionals since its inception in 2001.The authors describe the original design and intent of the Institute and its evolution over the last 18 years as both the context for leadership and leadership requirements have evolved. The Institute's framework, key features, principles, concepts and streams of learning are outlined along with summaries of research on personal and organizational impact. Lessons learned and recommendations are included in the areas of program design, registrant selection and organizational support. This paper will be of interest to those who wish to better understand how the context of leadership in healthcare is evolving and how leadership attributes and behaviours can be developed to meet current and new challenges.

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)0.015
metaresearch head score (Gemma)0.011
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: Other · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.018
Scholarly communication0.0180.009
Open science0.0020.008
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.001

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 head0.345
GPT teacher head0.399
Teacher spread0.054 · 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
GenreOther

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
Published2018
Admission routes3
Has abstractyes

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