Health problems of Nepalese migrants working in three Gulf countries
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».