Business Opportunities of Information and Communication Technologies (ICTs) in Health Services for Democratic Republic of Congo
Bibliographic record
Abstract
According to the United Nations Development Program, the Agenda for Sustainable Development Goals projects by 2030 is improving human well-being over the world by eliminating poverty, creating gender equality, preserving the environment, helping shared growth, as well as involving people at the core of the sustainable development Agenda. Democratic Republic of Congo (DRC) is in a fragile state where its legitimacy is threatened by the war in the east of the country, the destruction of social capital and social cohesion as well as the severe economic challenges which have been amplified with the political context. Nationally, this Agenda constitutes a real opportunity to accelerate the implementation of Information and Communication Technologies (ICTs) in the fields of health, education and public services in this country. This article shows how to turn the ICTs adapted for DRC into business opportunities and how various types of entrepreneurship can be implemented at the local and national levels, with a particular focus on medical drones. The article also proposes the guidelines to be followed for the success of such experiences both in the private and public sectors, as well as in international partnerships between developed, emerging and developing countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".