Testing a theoretical model of clinical nurses’ intent to stay
Bibliographic record
Abstract
BACKGROUND: Published theoretical models of nurses' intent to stay (ITS) report inconsistent outcomes, and not all hypothesized models have been adequately tested. Research has focused on cognitive rather than emotional determinants of nurses' ITS. PURPOSE: The aim of this study was to empirically verify a complex theoretical model of nurses' ITS that includes both affective and cognitive determinants and to explore the influence of relational leadership on staff nurses' ITS. METHODOLOGY: The study was a correlational, mixed-method, nonexperimental design. A subsample of the Quality Work Environment Study survey data 2009 (n = 415 nurses) was used to test our theoretical model of clinical nurses' ITS as a structural equation model. RESULTS: The model explained 63% of variance in ITS. Organizational commitment, empowerment, and desire to stay were the model concepts with the strongest effects on nurses' ITS. Leadership practices indirectly influenced ITS. PRACTICE IMPLICATIONS: How nurses evaluate and respond to their work environment is both an emotional and rational process. Health care organizations need to be cognizant of the influence that nurses' feelings and views of their work setting have on their intention decisions and integrate that knowledge into the development of retention strategies. Leadership practices play an important role in staff nurses' perceptions of the workplace. Identifying the mechanisms by which leadership influences staff nurses' intentions to stay presents additional focus areas for developing retention strategies.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".