Generational Differences in Acute Care Nurses
Bibliographic record
Abstract
UNLABELLED: Generational differences in values, expectations and perceptions of work have been proposed as one basis for problems and solutions in recruitment and retention of nurses. METHOD: This study used a descriptive design. A sample of 8207 registered nurses and registered practical nurses working in Ontario, Canada, acute care hospitals who responded to the Ontario Nurse Survey in 2003 were included in this study. Respondents were categorized as Baby Boomers, Generation X or Generation Y based on their birth year. Differences in responses among these three generations to questions about their own characteristics, employment circumstances, work environment and responses to the work environment were explored. RESULTS: There were statistically significant differences among the generations. Baby Boomers primarily worked full-time day shifts. Gen Y tended to be employed in teaching hospitals; Boomers worked more commonly in community hospitals. Baby Boomers were generally more satisfied with their jobs than Gen X or Gen Y nurses. Gen Y had the largest proportion of nurses with high levels of burnout in the areas of emotional exhaustion and depersonalization. Baby Boomers had the largest proportion of nurses with low levels of burnout. CONCLUSION: Nurse managers may be able to capitalize on differences in generational values and needs in designing appropriate interventions to enhance recruitment and retention 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.
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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".