Early retirement among Registered Nurses: contributing factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: This study explored the factors that influence nurses to retire early and the incentives that might encourage them to stay longer in employment. BACKGROUND: The increasing number of nurses taking early retirement reduces an already depleted nursing workforce. METHODS: A mail-out questionnaire was sent to 200 randomly selected nurses aged 45 and older, living in the Canadian province of Newfoundland and Labrador. SPSS descriptors were used to outline the data. Multiple t-tests, with a Bonferroni correction, 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. Staff nurses and a group of nurse managers/educators/researchers differed significantly in two reasons for leaving. The two groups also differed significantly in five of the incentives to stay. CONCLUSIONS: Findings from this study could prove useful for healthcare and government organizations developing retention strategies to forestall the predicted shortage 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.
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 | 0.002 | 0.000 |
| 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 | 0.000 | 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 it