Adaptation of French general practitioners for the management of nursing home patients during COVID-19 in 2020: a multilevel analysis
Bibliographic record
Abstract
BACKGROUND: To describe French general practioners' (GPs) adaptation strategies to ensure follow-up care of nursing home patients during the first wave of COVID-19 (May 2020) and to identify factors associated with each strategy. METHODS: A national cross-sectional study was conducted with online questionnaires in May 2020 among GPs practicing in France (metropolitan and overseas) and usually providing nursing home visits before pandemic. The outcome was defined as the GPs' adaptation strategies for managing nursing home patients and was categorized into four groups: Maintenance of Nursing Home Visits NHV (reference), Stopping NHV, Numeric adaptation (teleconsultations only), Mixed adaptation (NHV and teleconsultations). The probability of adaptation strategies was analyzed by multilevel logistic models in which the GPs represented level 1 and the counties level 2. We applied three random-intercept multilevel logistic models with the county of GP's practice as random effect. RESULTS: This analysis included 2,146 responses by GPs coming from 98 French counties. Overall, 40.4% of GPs maintained NHV, while other strategies were: Stopping visits (24.1%), Numeric adaptation (15.4%), Mixed adaptation (20.1%). Several individual (age, training GP, perceived status of being at high risk of severe COVID, compliance with temporary delegation of the patient's management) and territorial factors (excess mortality rate due to COVID-19, GPs' density, proportion of over-75s, presence of reinforcement measures for nursing home patients) were identified as associated with each strategy. CONCLUSIONS: This study highlights a rapid adaptation of general practice to keep supporting nursing home patients. Heterogeneity of adaptation strategies could reflect both the lack of national guidelines and the heterogeneity among GPs' usual practices. Policymakers should take actions at a territorial level (subnational) to strengthen support to nursing home patients considering adaptations to the local context of the pandemic outbreak and perspective of local actors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".