Bibliographic record
Abstract
Migration of Health workers has devastating consequences leading to loss of health workers in the nation of origin. This research was carried out to identify the push factors related to migration of Nurses from Nepal to other developed countries. A cross sectional descriptive study was conducted via different social medias. The data was collected from 67 migrated Nepalese Nurses to: Australia, USA, UK and Canada. Self-administered questionnaire in the form of “Google docs form” was used to collect data from respondents. The study showed that 70.15% of respondents were of 20-29 years of age. In the study, 38.80% of respondents were from Australia followed by 31.34% from USA, 16.43% from UK and 13.43% from Canada. When the researcher advised respondents to prioritize the major cause of migration by giving 1 to major and 8 to least responsible factor of migration, the study revealed that personal ambition (Mean: 3.18), lack of job and career opportunities in Nepal (Mean: 3.57), economical factors (Mean: 3.2), and job dissatisfaction (Mean: 4.90) are the main causes of migration among Nepalese Nurses. This study also showed that 55.22% of respondents were not satisfied with their job in Nepal. 53.74% and 43.28% of the respondents are satisfied and highly satisfied respectively with their job in abroad. It can also be concluded that lack of modern facilities merely is not only the motivating factor for migration among Nepalese nurses, age and personal ambition also play a role in migration.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".