Generational differences in distress, attitudes and incivility among nurses
Bibliographic record
Abstract
AIMS: The first research objective was to replicate the finding of Leiter et al. [(2008)Journal of Nursing Management, 16, 100-109.] of Generation X nurses (n=338) reporting higher levels of distress than Baby Boomer nurses (n=139). The second objective was to test whether Generation X nurses reported more negative social environments at work than did Baby Boomer nurses. BACKGROUND: Negative social environments can influence the quality of work and the experience of distress for nurses. Generational differences in the experience of distress and collegiality have implications for the establishment of healthy workplaces, recruitment and retention. METHODS: A questionnaire survey of nurses was organized by generation. Analyses of variance contrasted the scores on burnout, turnover intention, physical symptoms, supervisor incivility, coworker incivility and team civility. RESULTS: The results confirmed the hypotheses of Generation X nurses reporting more negative experiences than did Baby Boomer nurses on all measures. CONCLUSIONS: The negative quality of social encounters at work contributes to nurses' experience of distress and suggest conflicts of values with the dominant culture of their workplaces. IMPLICATIONS FOR NURSING MANAGEMENT: Proactive initiatives to enhance the quality of collegiality can contribute to retention strategies. Building collegiality across generations can be especially useful.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".