A contextual work‐life experiences model to understand nurse commitment and turnover
Bibliographic record
Abstract
AIMS: The aim of this study was to present a discussion and model depicting most effecting work-life experience contextual factors that influence commitment and turnover intentions for nurses in Sri Lanka. BACKGROUND: Increasing demand for nurses has made the retention of experienced, qualified nursing staff a priority for healthcare organizations and highlights the need to capture contextual work-life experiences that influence nurses' turnover decisions. DESIGN: Discussion paper. DATA SOURCES: This discussion paper and model is based on our experiences and knowledge of Sri Lanka and represents an integration of classic turnover research and commitment theory and others published between 1958 - 2017, contextualized to reflect the reality faced by Sri Lanka nurses. IMPLICATIONS FOR NURSING: The model presents a high-level view of intrinsic, extrinsic, personal and professional antecedents to nurse turnover where relevance can be used by researchers, policy makers, clinicians and educators to establish focused and limited scope models and examine comprehensive contexts. CONCLUSION: This model emphasizes the role that work-life experiences play to fortify (or weaken) nurses' motivation to remain committed to their organization, profession, family, and country. Understanding of contextual work-life influences on nurses' intent to stay should lead to evidence-based strategies that result in a higher number of nurses wanting to remain in the nursing profession and work in the health sector in Sri Lanka.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".