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Record W2132055971 · doi:10.1111/jan.12426

Aged over 50 years and practising: separation and changes in nursing practice among New Zealand's older Registered Nurses

2014· article· en· W2132055971 on OpenAlexaboutno aff
Nicola North, William Leung, Rochelle Lee

Bibliographic record

VenueJournal of Advanced Nursing · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceCohortMedicineQuarter (Canadian coin)Retrospective cohort studyNursingCohort studyFamily medicineWorkforce planningDemographyGerontologyGeographyPolitical science

Abstract

fetched live from OpenAlex

AIM: To describe temporary and permanent separation patterns and changes in nursing practice over 5 years, for the 2006 cohort of nurses aged ≥50 years in New Zealand. BACKGROUND: As ageing populations increase demand on nursing services, workforce projections need better information on work and retirement decision-making of large 'baby-boomer' cohorts. DESIGN: Retrospective cohort analysis using the Nursing Council of New Zealand administrative dataset. METHODS: A cohort of all nurses aged ≥50 years on the register and practising in 2006 (n = 12,606) was tracked until 2011. RESULTS: After 5 years, a quarter (n = 3161) of the cohort (equivalent to 8·4% of all 2006 practising nurses) was no longer practising. There were no significant differences in permanent separation rates between the ages of 50-58; between 18-54% of annual separations re-entered the workforce. On re-entry, 56% returned to the same clinical area. Annual separations from the workforce declined sharply during the global financial crisis and more of those leaving re-entered the workforce. In 2006, half the cohort worked in hospitals. After 5 years, the number of cohort nurses working in hospitals fell by 45%, while those in community settings increased by 12%. Over 5 years, weekly nursing practice hours declined significantly for every age-band. CONCLUSIONS: To retain the experience of older nurses for longer, workforce strategies need to take account of patterns of leaving and re-entering the workforce, preferences for work hours and the differences between the sub-groups across employment settings and practice areas.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.098
GPT teacher head0.465
Teacher spread0.368 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations16
Published2014
Admission routes1
Has abstractyes

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