<i>Une approche participative pour la prévention du diabète de type 2</i> chez les jeunes francophones du Nouveau-Brunswick
Bibliographic record
Abstract
Diabetes, a serious public health problem, is on the rise, claiming millions of victims. A considerable body of research exists on diabetes, but the development of effective primary prevention strategies is just beginning. This article presents the results of a project, based on an innovative approach where health professionals and community groups have come together to address the issue. The purpose of the project is to develop an intervention strategy for the prevention of type 2 diabetes directed at young francophones living in a minority environment in New Brunswick and adapted to their needs. Qualitative data were gathered from two focus groups and submitted for a content analysis. The process was evaluated. The young francophones have identified the school environment as ideal for intervention. According to them, the intervention should be adapted to the age of the youths. For the 5-to-13-year-old group, the intervention should target healthy eating habits and physical activity whereas for the 14-to-18-year-old group, the emphasis should be on preventing diabetes. The youth and the professionals acquired a greater understanding of the problem of diabetes and its prevention. Youth can now proceed to action, with appropriate guidance. The experience and knowledge of the professionals contributed to the development of the strategy. A shortage of dietitians in public health to work in the area of the prevention of diabetes has been noted.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".