French General Practitioners’ Adaptations for Patients with Suspected COVID-19 in May 2020
Bibliographic record
Abstract
In France, towards the end of the first lockdown, COVID-19 management was largely transferred from hospitals to primary care. Primary care actors adapted their practices to ensure patients' access to care, while limiting contamination. In this study, we aimed to identify patterns of adaptations implemented by French general practitioners (GPs) in May 2020 for outpatients with confirmed or suspected COVID-19, and factors associated with these adaptions. A French survey concerning care organization adaptations, and individual, organizational, and territorial characteristics, was sent to GPs. Data were analyzed by multiple correspondence analysis followed by agglomerative hierarchical clustering to identify GPs' adaptation clusters. A multinomial logistic regression model estimated the associations between clusters and individual, organizational, and territorial factors. Finally, 3068 surveys were analyzed (5.8% of French GPs). Four GPs' adaptation clusters were identified: autonomous medical reorganization (64.2% of responders), interprofessional reorganization (15.9%), use of hospital (5.1%), and collaboration with COVID-19 outpatient centers (14.8%). Age, practice type and size, and territorial features were significantly associated with adaptation clusters. Our results suggest that healthcare systems should consider organizational features of primary care to effectively deal with future challenges, including healthcare crises, such as the COVID-19 pandemic, but also those linked to epidemiologic and societal changes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".