Nursing theory and concept development: a theoretical model of clinical nurses’ intentions to stay in their current positions
Bibliographic record
Abstract
AIM: We describe a theoretical model of staff nurses' intentions to stay in their current positions. BACKGROUND: The global nursing shortage and high nursing turnover rate demand evidence-based retention strategies. Inconsistent study outcomes indicate a need for testable theoretical models of intent to stay that build on previously published models, are reflective of current empirical research and identify causal relationships between model concepts. DATA SOURCES: Two systematic reviews of electronic databases of English language published articles between 1985-2011. DISCUSSION: This complex, testable model expands on previous models and includes nurses' affective and cognitive responses to work and their effects on nurses' intent to stay. The concepts of desire to stay, job satisfaction, joy at work, and moral distress are included in the model to capture the emotional response of nurses to their work environments. The influence of leadership is integrated within the model. IMPLICATIONS FOR NURSING: A causal understanding of clinical nurses' intent to stay and the effects of leadership on the development of that intention will facilitate the development of effective retention strategies internationally. Testing theoretical models is necessary to confirm previous research outcomes and to identify plausible sequences of the development of behavioral intentions. CONCLUSION: Increased understanding of the causal influences on nurses' intent to stay should lead to strategies that may result in higher retention rates and numbers of nurses willing to work in the health sector.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".