Professional satisfaction among emigrated Nurses of Nepal: A cross-sectional web-based study
Bibliographic record
Abstract
Background: Professional satisfaction is nowadays leading cause in increasing emigration of Nurses of Nepal. Thus, this research was conducted to identify the level of professional satisfaction among emigrated nurses in Nepal. Methods: A cross-sectional descriptive study was conducted among 102 emigrated Nepalese Nurses staying in the USA, UK, UAE, Australia, Denmark and Canada. A web-based semi-structured questionnaire based on the McCloskey/ Mueller Satisfaction Scale was used to collect data from participants. Data were analysed using statistical package for social science (SPSS) version 24. The descriptive and inferential statistical analysis was conducted to interpret the data. Results: The participants for the study were from Australia (57.8%) followed by USA (25.5%), UK (10.8%), from Canada (2.9%), UAE (2%) and Denmark (1%). It was found that 74.5% of participants were from 25-30 years of age group. The emigrated Nurses working in Australia were highly satisfied (74.6%) with their job. This study showed that the majority (68.63%) of participants had a high satisfaction level in their job abroad where 2.9% had a lower satisfaction level. Conclusion: It is concluded that Nepalese nurses have a high professional satisfaction level working in abroad. So, the government needs to plan the retention of nurses in the countries considering the facilities and motivation provided in the emigrant countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".