General practitioners' attitude towards cooperation with other health professionals in managing patients with multimorbidity and polypharmacy: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Cooperation between general practitioners (GPs) and other healthcare professionals appears to help reduce the risk of polypharmacy-related adverse events in patients with multimorbidity. OBJECTIVES: To investigate GPs profiles according to their opinions and attitudes about interprofessional cooperation and to study the association between these profiles and GPs' characteristics. METHODS: Between May and July 2016, we conducted a cross-sectional survey of a panel of French GPs about their management of patients with multimorbidity and polypharmacy, focussing on their opinions on the roles of healthcare professionals and interprofessional cooperation. We used agglomerative hierarchical cluster analysis to identify GPs profiles, then multivariable logistic regression models to study their associations with the characteristics of these doctors. RESULTS: 1183 GPs responded to the questionnaire. We identified four profiles of GPs according to their declared attitudes towards cooperation: GPs in the 'very favourable' profile (14%) were willing to cooperate with various health professionals, including the delegation of some prescribing tasks to pharmacists; GPs in the 'moderately favourable' profile (47%) had favourable views on the roles of health professionals, with the exception for this specific delegation of the task; GPs from the 'selectively favourable' profile (27%) tended to work only with doctors; GPs from the 'non-cooperative' profile (12%) did not seem to be interested in cooperation. Some profiles were associated with GPs' ages or participation in continuing medical education. CONCLUSION: Our study highlights disparities between GPs regarding cooperation with other professionals caring for their patients and suggests ways to improve cooperation.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".