Contrasting burnout, turnover intention, control, value congruence and knowledge sharing between Baby Boomers and Generation X
Bibliographic record
Abstract
AIM(S): This paper examines the contrasting role of work values for nurses from two generations: Baby Boomers and Generation X. BACKGROUND: Differences among nurses regarding core values pertaining to their work has a potential to influence the quality of their work life. These differences may have implications for their vulnerability to job burnout. EVALUATION: The analysis is based upon questionnaire surveys of nurses representing Generation X (n = 255) and Baby Boomers (n = 193) that contrasted their responses on job burnout, areas of work life, knowledge transfer and intention to quit. KEY ISSUE(S): The analysis identified a greater person/organization value mismatch for Generation X nurses than for Baby Boomer nurses. Their greater value mismatch was associated with a greater susceptibility to burnout and a stronger intention to quit for Generation X nurses. CONCLUSION(S): The article notes the influence of Baby Boomer nurses in the structure of work and the application of new knowledge in health care work settings. Implications for recruitment and retention are discussed with a focus on knowledge transfer activities associated with distinct learning styles. IMPLICATIONS FOR NURSING MANAGEMENT: Understanding value differences between generations will help nursing managers to develop more responsive work settings for nurses of all ages.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".