Antecedents of Turnover Intention Behavior among Nurses: A Theoretical Review
Bibliographic record
Abstract
The employees’ turnover intention is one of the most popular subjects in the field of Human Resource Management. Moreover, the turnover problematic phenomenon is also still one of the most costly issues for HR managers in their efforts on human capital. Although turnover intention has been one of the most researched phenomenon in Human Resource Management (HRM), researchers still return to restudying this phenomenon because of its impact on service quality in any organization, moreover turnover intention has direct and indirect costs, both costs are critical, complicated, and serious. While the phenomenon of turnover intention in the nursing sector has a more serious impact than on any other sector, it has been recognized in both developing and developed countries. Organizational factors (Leadership, Pay Level, and Advancement Opportunities) have excessive impact on turnover intention among employees. Thus, this conceptual paper focuses on the organizational factors as the determinant of turnover intention among registered nurses. Approach: The literature was explored to acknowledge the accessible relationships among cross organizational factors (Leadership, Pay Level, and Advancement Opportunities) and turnover intention among registered nurses public hospitals. Conclusions: This conceptual paper provides an updated review of the literature on organizational factors and turnover intention. The practical implications as well as academic contributions were also presented.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".