To which non-physician health professionals do French general practitioners refer their patients to and what factors are associated with these referrals? Secondary analysis of the French national cross-sectional ECOGEN study
Bibliographic record
Abstract
BACKGROUND: Multiprofessional practice is a key component in primary care. Examining general practitioner (GP) referral frequency to non-physician health professionals (NPHP) can provide information about how primary care is organised and works which is useful for policymakers. Our study aimed to describe French GP referral frequency to various NPHPs in France and identify associated factors. METHODS: This is an ancillary study to the observational, cross-sectional (ECOGEN) study conducted in 2011/2012 in France among 128 GPs. Data about consultations using the standardised International Classification of Primary Care (ICPC-2), and patient and GP characteristics were collected from 20,613 GP consultations. Referrals were identified through inductive and deductive approaches using ICPC-2 codes, keywords, and deep, open manual searches. Referral frequency was described overall and per NPHP. Patient, GP, and consultation-related factors associated with referral rates were described for the three most frequently identified NPHPs. To minimise potential sources of bias, this observational study followed the STROBE guidelines. RESULTS: French GPs referred 6.8% of patients to NPHPs, with physiotherapists, podiatrists, and nurses accounting for 85.2% of referrals. Older patients, retired patients, multiple health problems managed, and longer consultation durations were found to be associated with higher referral rates (p < 0.001). Specific trends were observed for nurse, physiotherapist, and podiatrist referrals. Women (p < 0.001) and regular patients (p = 0.002) were more likely to receive physiotherapy referrals while people with no professional activity were less likely (p < 0.001). Female GPs and those working in urban practices were more likely to issue a physiotherapy referral (p < 0.001), while GPs working in rural practices (p < 0.001) and those with higher annual consultation numbers (p = 0.002) were more likely to refer to a nurse. Working in multiprofessional centres appeared to have little impact on referral rates, being only slightly associated with podiatrist referrals (p = 0.003). CONCLUSIONS: Referral frequency is more associated with patient characteristics and clinical situations than GP-related factors suggesting patients needing referral most are most often referred. Furthermore, the three NPHPs that GPs refer to the most are those for which a referral is required for reimbursement in France, suggesting that health system legislation and NPHP reimbursement are strong determinants for referrals.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".