Programmes de lutte contre le diabète dans six pays européens et au Canada
Bibliographic record
Abstract
Seventeen of the 27 European Union countries have established programs and initiatives to counteract the increasing rate of diabetes in Europe. Luxembourg has not instituted such an initiative at a national level but is considering a national scheme. This article presents several national diabetes policies from other states. The information in these national schemes can be used to assist in the development of a national diabetes program in Luxembourg. Seven national diabetes programs, from The Netherlands, England, Austria, Germany, France, Belgium and Canada, were analyzed. We aimed to identify the most important principles underlying these programs and what makes them successful. The national health policies encompass 3 dimensions: psychological, social and economic. Some key determinants were identified. The most successful diabetes programs promote quality of care and services, early detection and the autonomy of people through the patient's therapeutic education. Other identified determinants are the establishment of an efficient information system, enabling people with diabetes to have access to excellent services and educational information. The system also allows health professionals to easily follow up their diabetes patients and provides a tool for evaluating and developing multidisciplinary competences for professionals.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".