General Practitioners' Propensity to Cooperate With Other Health Professionals in the Management of Patients With Multimorbidity and Polypharmacy: A Cross-sectional Study.
Bibliographic record
Abstract
Abstract Background. Cooperation between general practitioners (GPs) and other health professionals appears to help reduce the risk of adverse events linked to polypharmacy for patients with multimorbidity. We investigated the existence of different GP profiles according to their opinions and behaviors about such cooperation and studied the association between these profiles and the GPs’ characteristics and deprescribing behaviors. Methods. Between May and July 2016, we performed a cross-sectional survey in a panel of French GPsabout their management of patients with multimorbidity and polypharmacy, focusing specifically on their opinions of healthcare professionals’ roles and interprofessional cooperation. We used an agglomerative hierarchical cluster analysis to identify GP profiles and then multivariable logistic regression models to study their associations with these doctors' characteristics and deprescribing behaviors. Results. We identified four profiles of GPs according to their cooperation propensities: GPs from the “intensive” profile (14%) were favorable to cooperating with various health professionals, including delegating some prescribing tasks to pharmacists; GPs from the "moderate" profile (47%) had favorable opinions about health professionals’ roles, except for this specific task delegation; GPs from the "selective" profile (27%) tended to work only with physicians; GPs from the "low cooperation" profile (12%) didn’t appeared interested in cooperation. These profiles were associated with different professional characteristics. Conclusions. Current health policies encourage interprofessional cooperation for the management of patients with multimorbidity. Our study provides information for understanding disparities among GPs regarding working with other professionals who deal with their patients and suggests possible ways to improve cooperation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| 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".