Historicizing medical drones in Africa: a focus on Ghana
Notice bibliographique
Résumé
While the genesis of the drone technology is not clear, one thing is ideal: it emerged as a military apparatus and gained much attention during major wars, including the two world wars. Aside being used in combats and to deliver humanitarian services, drones have also been used extensively to kill both troops and civilians. Revolutionized in the 19th century, the drone technology was improved to be controlled as an unmanned aerial devices to mainly target troops. A new emerging field that has seen the application of the drone technology is the healthcare sector. Over the years, the health sector has increasingly relied on the device for timely transportation of essential articles across the globe. Since its introduction in health, scholars have attempted to address the impact of drones on healthcare across Africa and the world at large. Among other things, it has been reported by scholars that the device has the ability to overcome the menace of weather constraints, inadequate personnel and inaccessible roads within the healthcare sector. This notwithstanding, data on drones and drone application in Ghana and her healthcare sector in particular appears to be little within the drone literature. Also, few attempts have been made by scholars to highlight the use of drones in African countries. By using a narrative review approach, the current study attempts to address the gap above. Using this approach, a thorough literature search was performed to locate and assess scientific materials that focus on the application of drones in the military field and in the medical systems of Africa and Ghana in particular. With its sole responsibility to deliver items, stakeholders of health across several parts of the world have relied on drones to transport vital articles to health centers. Countries like Senegal, Madagascar, Rwanda and Malawi encouraged Ghana to consider the application of drones in her mainstream healthcare delivery. Findings from the study have revealed that Ghana’s adoption of the drone policy has enhanced the timely delivery of products such as test samples, blood and Personal Protective Equipment to various health centres and rural areas in particular. Drones have contributed to the delivery of equity in healthcare delivery in Ghana. We conclude that with the drone policy, the continent has the potential to record additional successes concerning the over-widened gap in healthcare between rural and urban populations.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».