General practitioners’ acceptability to handle unscheduled visits: a french nationwide cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: The increasing demand for emergency department (ED) services has led to overcrowding, impacting the quality and accessibility of healthcare. The objectives were to evaluate how general practitioners (GPs) manage unscheduled visits, including patients redirected from emergency departments and/or medical dispatch centers, and to identify potential barriers to accepting these patients. METHODS: We conducted a cross-sectional survey in France from April 2020 to March 2021, utilizing a self-administered, web-based, anonymous pre-tested questionnaire distributed among the members of professional society and social network. The questionnaire was divided into three sections, namely: (1) demographics of GP respondents; (2) current organization for handling unscheduled visits, and (3) potential barriers to implementing dedicated schedule slots for patients redirected from EDs. We used multivariable logistic regression to investigate associations between personal and practice characteristics. RESULTS: A total of 1,317 GPs participated in the survey. The majority were female (59%), aged between 31 and 40 years (43%) and located in semi-rural areas (66%). Overall, 89% reported to allocate specific slots for unscheduled care visits. However, only 16% had their slots open to emergency/dispatch center-referred patients. The majority (83%) of GPs showed no interest in setting aside dedicated slots for ED redirected patients, 62% were against sharing information about their unscheduled visit slots with EDs, and 88% opposed the automatic scheduling of ED patients into their clinics. The main barriers to the implementation of dedicated slots for unscheduled visit were a lack of interest (46%), time constraints (48%), and organizational challenges (9%). GPs with smaller patient bases and those using computerized systems showed greater openness to communicate availability for unscheduled care. CONCLUSION: Most French GPs surveyed, representing approximatively 2% of the overall French GP population, integrate daily slots for unscheduled visits into their practice but they are typically reserved for their existing patient base. The survey further revealed that two-thirds of these GPs are unwilling to communicate the availability of these slots to EDs. Additionally, an even larger proportion would refuse to allow automatic scheduling of ED patients into their clinics if redirection strategies were implemented at their local hospitals.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".