Historicizing medical drones in Africa: a focus on Ghana
Bibliographic record
Abstract
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".