Les soins primaires face à la COVID-19 : une comparaison Belgique, France, Québec et Suisse
Bibliographic record
Abstract
INTRODUCTION: Facing COVID-19, most of health care system first responded with the confinement of the population and an increase of intensive care resources. Primary care was then mobilized variably and more or less coordinated. PURPOSE OF RESEARCH: Comparing the involvement of primary care in four francophone regions with similar primary care to draw lessons for reforms directions in light of the COVID experience. RESULTS: Mobilization of primary care actors was important, heterogeneous and linked to local context and previous dynamics at the territorial level or the practice level except in Quebec where primary care is governed by health authorities. The creation of COVID centers was systematic as "warm practices" in Quebec or left to the initiative of local stakeholders more or less supported by health authorities. Teleconsultation, largely dominated by the use of the telephone, was implemented everywhere, generally supported by flexible and adapted pricing. The performance of diagnostic tests such as vaccination by new professionals within a legal, financial and simple training framework is a major area for improvement. Information systems to assess local needs were insufficient everywhere. CONCLUSION: The definition of primary care governance methods and, in particular, the link between professionals and public health operators in the four areas studied is a priority area for improvement at both local and national levels.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".