Registered nurses' psychological capital: A scoping review
Bibliographic record
Abstract
AIMS: The aim was to examine the extent and scope of empirical research concerning registered nurses' psychological capital. BACKGROUND: In a time of global nursing shortage, identifying variables that could positively contribute to the retention of the nursing workforce is essential. Prior research has shown that psychological capital correlates positively with employees' better performance and well-being. DESIGN: A scoping review. DATA SOURCES: A systematic literature search was conducted in the following databases: PubMed, CINAHL, PsycINFO, Web of Science and Scopus covering the period from 1 January 2005 to 7 May 2023. REVIEW METHODS: The JBI methodological guidance for scoping reviews was followed. The results were summarized narratively. RESULTS: A total of 111 studies reported in 114 peer-reviewed articles were included. Studies were carried out across 20 countries, with the majority from China (45), Australia (nine), Pakistan (nine), Canada (eight), South Korea (eight) and the United States (eight). A positive correlation was found between registered nurses' psychological capital and desirable work-related outcomes, such as work engagement, commitment and retention intention. CONCLUSION: A comprehensive overview of research evidence suggests that psychological capital is associated with many positive work-related outcomes and might therefore be a valuable resource for reducing nurse turnover.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 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".