International Cooperation and the Challenge of Internet Accessibility in Caribbean Territories: Example of a Collaborative Platform Between the University Hospital of Martinique and Ramón González Coro Hospital in Cuba, Through the French PRPH-3 Program
Bibliographic record
Abstract
BACKGROUND: Martinique shares with the other Caribbean countries specific public health issues, particularly in the diagnostic and therapeutic management of cancer patients. Mutualization of human and material resources by promoting cooperation is the most appropriate response to the challenges of the health systems of the Caribbean territories. Through the French PRPH-3 program, we propose to set up a collaborative digital platform adapted to the specificities of the Caribbean to strengthen professional links and skills in oncofertility and oncosexology and reduce inequalities in access to reproductive and sexual health care for cancer patients. METHODS: Within the context of this program, we have developed of an open-source platform based on a Learning Content Management System (LCMS), with an operating system developed by UNFM for low speed internet. LO libraries have been created and interaction between trainers and learners were done in asynchronous mode. This training management platform is based on: a TCC learning system (Training, Coaching, Communities); a web-hosting with pedagogical engineering appropriate to low bandwidth; a reporting system and a responsibility for processing. RESULTS: We have carried out a flexible, multilingual and accessible digital learning strategy functionality called e-MCPPO according to low-speed internet ecosystem. In close connection with the e-learning strategy we conceived (i) a multidisciplinary team; (ii) an appropriate training program for expert health professionals and (iii) a responsive design. DISCUSSION AND CONCLUSION: This low-speed web-based infrastructure allows communities of experts to cooperate in creating, validating, publishing and managing academic learning content. The self-learning modules provide the digital layer for each learner to extend their skills. Learners, as well as trainers, would gradually take ownership of this platform and encourage its promotion. Innovation in this context is both technological (low-speed Internet broadcasting, free interactive software) and organizational (moderating educational resources). This collaborative digital platform is unique in its form and content. This challenge could contribute to the digital transformation of the Caribbean ecosystem for capacity building in this specifics topics.
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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 | 0.002 | 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".