Linking Psychological Capital, Structural Empowerment and Perceived Staffing Adequacy to New Graduate Nurses' Job Satisfaction
Bibliographic record
Abstract
Reports indicate that new graduate nurses (NGNs) are experiencing stressful work environments, affecting job satisfaction and retention in current positions. New nurses are a health human resource that must be retained in order to ensure the replacement of retiring nurses, and to address impending shortages. As a result, creating supportive work environments that promote NGNs’ job satisfaction may play an important role in the retention and recruitment of skilled, satisfied nursing staff. The purpose of this study was to test the relationships between new graduates’ self-reported psychological capital (PsyCap), access to empowerment structures, perceptions of staffing adequacy and job satisfaction. A secondary analysis of data collected using a non-experimental predictive survey design was conducted on a sample of 205 NGN’s working in the province of Ontario. Hierarchical multiple regression was used to test the study hypothesis. Results indicated that PsyCap, structural empowerment and perceptions of adequate nurse staffing were significant independent predictors of NGNs’ job satisfaction (β= .38, β= .50 and β=.17 respectively), explaining 41% of the total variance. Study findings suggest that support for personal and structural resources in the workplace will enhance overall job satisfaction in new 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".