Predictors of the decision to retire among nurses in Spain
Bibliographic record
Abstract
Background: Nurses typically retire in their late 50s and since nursing shortages exist in most countries understanding nursing staff decisions to retire might open up possibilities of encouraging and supporting them to remain in the workforce longer. Objective: This study examines potential predictors of retirement intentions amongst nurses working in Spain. Population: All registered nurses in Spain with 50 years old or more. Methods: Survey. Data were collected with the collaboration of the regional nursing associations in Spain using anonymous online questionnaires employed to nursing staff (n=497) for those who are 50 years or older. Results: Nurses indicated their interest in retiring, their planning for retirement, and their expectations for retiring. Results show that retirement intentions were higher in nursing staff that were older, experienced higher levels of burnout, indicated poorer levels of self-reported health, and reported greater job demands and more negative work attitudes (less affective commitment, job involvement, work engagement). The majority of these were “push†factors which are related to dissatisfaction in the workplace. Conclusion: Organizations can and should create age-friendly workplaces enabling them to cope with the nursing shortage and workplaces can be changed to better accommodate the needs and expectations of older employees.
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 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".