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Record W2121758131 · doi:10.12927/cjnl.2010.21830

Transformational Leadership to Promote Cross-Generational Retention

2010· article· en· W2121758131 on OpenAlexaffvenue
Vanessa M. Lobo

Bibliographic record

VenueNursing leadership · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTransformational leadershipBaby boomersWorkforceNursing managementTransactional leadershipLeadership styleGeneration xNursingPublic relationsPsychologyEconomic shortageGeneration yPopulationSociologyPolitical scienceBusinessMedicineMarketingDemographic economicsGovernment (linguistics)

Abstract

fetched live from OpenAlex

As the current nursing shortage intensifies under the weight of an aging population, retention of front-line staff is becoming paramount. Studies have consistently demonstrated that the leadership style of nurse managers plays a significant role to this end. This paper describes some of the challenges that managers encounter in their dealings with the contemporary multigenerational workforce - including the baby boomers, generation X and generation Y (the "millennials"). A review of research findings suggests the insufficiency of a single leadership approach to nurse management compared to more tailored generational strategies. Application of the transformational leadership model provides the background and tenets from which solutions are proposed for multigenerational management.

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)0.005
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.194
GPT teacher head0.284
Teacher spread0.090 · 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

Citations9
Published2010
Admission routes2
Has abstractyes

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