Soins primaires et COVID-19 en France : apports d’un réseau de recherche associant praticiens et chercheurs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it