[The society "Santé en Français": a successful Canadian model for partnership].
Bibliographic record
Abstract
Francophone Canadians living in a context where French is a minority language have poor access to health services in their native language. To remedy this situation, the Canadian Government has adopted a networking model inspired from the strategy "Towards Unity For Health" (TUFH) elaborated by the World Health Organization (WHO). This model, used since 2001, has given rise to a large number of partnership networks covering the entire regions where minority Francophones live. In this model five key stakeholders in health are being involved: health professionals, communities, managers of health care institutions, educational institutions and governments. The Canadian Federal Government, in close collaboration with communities, directed the project through two non-for-profit agencies: the Sociéte Santé en Français (SSF) and the Consortium National de Formation en Santé (CNFS), sharing a common vision and aiming at improving access to health services and hence health status of minority Francophones. The networking following the TUFH model created a lot of opportunities as well as many challenges to overcome.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".