Bibliographic record
Abstract
AIM: This study explored various factors and income sources that registered nurses believe are important in retirement planning. BACKGROUND: In many countries worldwide, many registered nurses are approaching retirement age. This raises concerns related to the level of preparedness of retiring nurses. METHODS: A mail-out questionnaire was sent to 200 randomly selected nurses aged 45 and older. SPSS descriptors were used to outline the data. Multiple t-tests were conducted to test for significant differences between selected responses by staff nurses and a group of nurse managers, educators and researchers. RESULTS: Of 124 respondents, 71% planned to retire by age 60. Only 24% had done a large amount of planning. The top four planning strategies identified were related to keeping healthy and active, both physically and mentally; a major financial planning strategy ranked fifth. Work pensions, a government pension and a personal savings plan were ranked as the top three retirement income sources. No significant differences were found between the staff nurse and manager groups on any of the items. IMPLICATIONS FOR NURSING MANAGERS/CONCLUSIONS: The results of this study suggest that managers' preparation for retirement is no different from that of staff nurses. All nurses may need to focus more on financial preparation, and begin the process early in their careers if they are to have a comfortable and healthy retirement. Nurse managers are in a position to advocate with senior management for early and comprehensive pre-retirement education for all nurses and to promote educational sessions among their staff. Managers may find the content of this paper helpful as they work with nurses to help them better prepare for retirement. This exploratory study adds to the limited amount of research available on the topic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".