Prevention in healthcare: France and Quebec face similar challenges
Bibliographic record
Abstract
Resume De nombreux enjeux se posent pour l’avenir des systemes de sante en France et au Quebec. Ces enjeux, similaires des deux cotes de l’Atlantique, sont caracterises par des transitions (demographique, epidemiologique et technologique), qui auront une incidence sur les pratiques de soins et de prevention. Le vieillissement des populations, l’amelioration de la qualite de vie, le deficit projete de certaines professions de la sante sont parmi les phenomenes qui les caracterisent et qui mettent a l’epreuve le systeme de sante. Dans ce contexte, il sera d’autant plus necessaire d’agir en amont des problemes de sante et d’investir les efforts du cote de la prevention. Les professionnels du soin sont directement interpelles pour mettre en œuvre ou pour participer aux actions de prevention. Dans les contextes de transformation des deux systemes, quatre types de leviers ou incitatifs ont avantage a etre mis de l’avant pour que la prevention s’integre dans les pratiques de soins : 1) le levier de la gouvernance, qui etablit la coherence globale des actions en liant les interventions propres au systeme de soins et celles qui se developpent dans le cadre d’une strategie de sante ; 2) le levier professionnel, qui cible la valorisation des competences sur le plan des pratiques de prevention et du leadership ; 3) le levier organisationnel, qui favorise le developpement de competences collectives autour de la prevention ; 4) le levier financier, qui oblige a explorer de nouveaux modes de remuneration et d’incitatifs soutenant la prevention.Prat Organ Soins 2009;40(4):275-282
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".