La place de la prévention dans les soins de santé primaires : comparaison de la France, du Québec et du Royaume-Uni
Bibliographic record
Abstract
Effective prevention within primary health care (PHC) is a key factor in ensuring the health and well-being of the population. However, in 2021, the report from the French Court of Audit (Cour des Comptes) judged the results of prevention policy in France to be mediocre overall. This study describes and compares the place of prevention in PHC in France, Quebec and the UK, to identify potential improvement pathways. Data from the literature search were collected according to the PHC study model, published by D.Kringos in 2010, adjusted to prevention. Some health indicators in Quebec and the UK suggest that their systems are more efficient. The three systems are similar, but a number of improvement pathways have been identified. A 10-year policy (Quebec, UK) and a multi-ministerial policy (Quebec) would improve the coherence of actions. An operational plan, with predefined objectives and budget, would improve efficiency (Quebec). The development of multidisciplinary structures is conducive to communication and trust between professionals. Entrusting preventive acts to a variety of healthcare professionals improves equality of access (Quebec, UK). Reorganizing the structure to create a local link between the field and central government is beneficial for adapting policy to local needs (Quebec, UK). The three countries have the same inspirations, but the manner and progress of implementation is different. Regular international comparison can be a source of inspiration for mutual improvement of systems.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".