Soins primaires et COVID-19 en France : apports d’un réseau de recherche associant praticiens et chercheurs
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 epidemic represented a major challenge for the primary care sector. We present the results of an interprofessional collaborative research endeavor conducted by the ACCORD network to describe primary care actors' and organizations' response to the first wave of the epidemic and national lockdown in France. METHODS: This work draws from quantitative and qualitative material. The quantitative data results from the cross-analysis of the six online surveys carried out by the ACCORD network between March and May 2020, among general practitioners, midwives, and multi-professional primary care organizations in France. This data was enriched by collective multi-professional and multi-disciplinary exchanges conducted in virtual focus groups during an online seminar. RESULTS: There was a significant decrease in primary care activity during the first wave of the epidemic. Many primary care actors adapted their organizations to lower the risk of coronavirus transmission while maintaining access and continuity of care. Professionals received and used information from multiple sources. The crisis revealed both the importance and the diversity of local networks of exchange and collaboration. CONCLUSIONS: Primary care actors adapted quickly and with important local variability to the COVID epidemic, highlighting the importance of pre-existing organizations and collaborations at the local level.
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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 | 0.030 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".