Health problems of Nepalese migrants working in three Gulf countries
Bibliographic record
Abstract
BACKGROUND: Nepal is one of the largest suppliers of labour to countries where there is a demand for cheap and low skilled workers. In the recent years the Gulf countries have collectively become the main destinations for international migration. This paper aims to explore the health problems and accidents experienced by a sample of Nepalese migrant in three Gulf countries. METHODS: A cross-sectional survey was conducted among 408 Nepalese migrants who had at least one period of work experience of at least six months in any of three Gulf countries: Qatar, Saudi Arabia and United Arab Emirates (UAE). Face to face questionnaire interviews were conducted applying a convenience technique to select the study participants. RESULTS: Nepalese migrants in these Gulf countries were generally young men between 26-35 years of age. Unskilled construction jobs including labourer, scaffolder, plumber and carpenter were the most common jobs. Health problems were widespread and one quarter of study participants reported experiencing injuries or accidents at work within the last 12 months. The rates of health problems and accidents reported were very similar in the three countries. Only one third of the respondents were provided with insurance for health services by their employer. Lack of leave for illness, cost and fear of losing their job were the barriers to accessing health care services. The study found that construction and agricultural workers were more likely to experience accidents at their workplace and health problems than other workers. CONCLUSION: The findings suggest important messages for the migration policy makers in Nepal. There is a lack of adequate information for the migrants making them aware of their health risks and rights in relation to health services in the destination countries and we suggest that the government of Nepal should be responsible for providing this information. Employers should provide orientation on possible health risks and appropriate training for preventive measures and all necessary access to health care services to all their workers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".