Why older nurses leave the workforce and the implications of them staying
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To identify factors that motivate older nurses to leave the workforce. BACKGROUND: As many older nurses are now reaching retirement age and will be eligible for government-funded pensions, governments are concerned about the impending financial burden. To prepare for this scenario, many are looking at increasing the age of retirement to 67 or 70 years. Little is known about how this will affect the continuing employment of older nurses and the consequences for employers and the nurses themselves if they remain longer in the workforce. DESIGN: Prospective randomised quantitative survey study. METHODS: The Mature Age Workers Questionnaire, Job Descriptive Index and Job in General Scale were used to measure job satisfaction, intention to retire and factors encouraging retirement in registered nurses aged 45 years and over (n = 352) in Australia (July-August 2007). RESULTS: There were 319 respondents. The mean age proposed for leaving the workforce was 61·7 years. Key motivators were: financial considerations (40·1%), primarily financial security; nurse health (17·4%) and retirement age of partner (13·3%). CONCLUSIONS: Older nurses are leaving the workforce prior to retirement or pension age, primarily for financial, social and health reasons, taking with them significant experience and knowledge. As financial considerations are important in older nurses decisions to continue to work, increasing the age of retirement may retain them. However, consideration will need to be given to ensure that they continue to experience job satisfaction and are physically and mentally able to undertake demanding work. RELEVANCE TO CLINICAL PRACTICE: Increasing retirement age may retain older nurses in the workforce, however, the impact on the health of older nurses is not known, nor is the impact for employers of older nurses continuing to work known. Employers must facilitate workplace changes to accommodate older nurses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".