The relationship between structural empowerment and psychological empowerment for nurses: a systematic review
Bibliographic record
Abstract
AIM: To describe the findings of a systematic review examining the relationship between structural empowerment and psychological empowerment for registered nurses (RNs). BACKGROUND: Workplace empowerment research reveals a link between empowerment and positive work behaviours and attitudes. Research demonstrating the essential relationship between structural empowerment and psychological empowerment will provide direction for future interventions aimed at the development of a strong and effective health care sector. METHODS: Published research articles examining structural empowerment and psychological empowerment for nurses were selected from computerized databases and selected websites. Data extraction and methodological quality assessment were completed for the included research articles. RESULTS: Ten papers representing six studies reveal significant associations between structural empowerment and psychological empowerment for RNs. IMPLICATIONS FOR NURSING MANAGEMENT: Creation of an environment that provides structural empowerment is an important organizational strategy that contributes to RNs' psychological empowerment and ultimately leads to positive work behaviours and attitudes. Critical structural components of an empowered workplace can contribute to a healthy, productive and innovative RN workforce with increased job satisfaction and retention.
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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".