Empowerment, engagement and perceived effectiveness in nursing work environments: does experience matter?
Bibliographic record
Abstract
AIMS: We examined the impact of empowering work conditions on nurses' work engagement and effectiveness, and compared differences among these relationships in new graduates and experienced nurses. BACKGROUND: As many nurses near retirement, every effort is needed to retain nurses and to ensure that work environments are attractive to new nurses. Experience in the profession and generational differences may affect how important work factors interact to affect work behaviours. METHODS: We conducted a secondary analysis of survey data from two studies and compared the pattern of relationships among study variables in two groups: 185 nurses 2 years post-graduation and 294 nurses with more than 2 years of experience. RESULTS: A multi-group SEM analysis indicated a good fit of the hypothesized model. Work engagement significantly mediated the empowerment/effectiveness relationship in both groups, although the impact of engagement on work effectiveness was significantly stronger for experienced nurses. CONCLUSIONS: Engagement is an important mechanism by which empowerment affects nurses feelings of effectiveness but less important to new graduates' feelings of work effectiveness than empowerment. Implications for nursing management Managers must be aware of the role of empowerment in promoting work engagement and effectiveness and differential effects on new graduates and more seasoned nurses.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".