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Towards an integrated approach for the management of ageing nurses

2006· review· en· W1964060077 on OpenAlexafffund
Mélanie Lavoie‐Tremblay, Linda O’Brien‐Pallas, Chantal Viens, Louise Hamelin Brabant, Céline Gélinas

Bibliographic record

VenueJournal of Nursing Management · 2006
Typereview
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversité LavalCanadian Foundation for Healthcare ImprovementUniversity of TorontoMcGill University
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsIncentiveNursing managementWorkforcePresentation (obstetrics)NursingAgeingIdentification (biology)Ageing societyWorkforce managementMedicinePsychologyGerontologyPolitical science

Abstract

fetched live from OpenAlex

AIM: The objective of this study is to provide an overview of the ageing of the nursing workforce and to explore retention strategies centred on the entire professional life and on all age groups. BACKGROUND: The presence of an increasing proportion of ageing workers presents a major challenge to the nursing profession. Evaluation Presentation of theories about the development of a healthy workplace leads to the identification of a framework on which managers can base their management decisions. KEY ISSUES: Examples of incentives relating to the framework are presented which were expressed by both nurses under 50 years of age and those over 50. CONCLUSIONS: Introducing incentives centred on all age groups provides an opportunity to create a healthy workplace for all generations of nurses.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.921
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.404
Teacher spread0.315 · 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 teacher head, not a consensus.

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

Citations43
Published2006
Admission routes2
Has abstractyes

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