Engaging new nurses: the role of psychological capital and workplace empowerment
Bibliographic record
Abstract
The purpose of this study was to test a hypothesised model linking perceptions of workplace empowerment and psychological capital (PsyCap) to new graduate nurses’ work engagement by integrating theories of empowerment, PsyCap and work engagement. In response to the nursing shortage, efforts are needed to retain nurses by creating empowering work environments that leverage employee PsyCap to foster work engagement. A secondary analysis of data ( n = 205) from a study by Laschinger et al. (2012) was conducted to test the hypothesised model. Hierarchical multiple linear regression analysis was used to test the influence of empowerment and psychological capital on new graduate nurses’ work engagement. Measures of structural empowerment (Conditions of Work Effectiveness Questionnaire-II), PsyCap (Psychological Capital Questionnaire) and work engagement (Utrecht Work Engagement Scale) were used. The hypothesised model was supported. The combined effect of workplace empowerment and PsyCap explained 38% of the variance in new nurses’ work engagement. Workplace empowerment and PsyCap were significant independent predictors of work engagement ( β = 0.45 and 0.36, p < 0.05, respectively). The results suggest that the combination of personal and organisational resources is related to greater work engagement among new graduate 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 | 0.003 | 0.011 |
| 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) | 0.002 | 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".