Business Opportunities of Information and Communication Technologies (ICTs) in Health Services for Democratic Republic of Congo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it