How the relationships between general practitioners and intensivists can be improved: the general practitioners' point of view
Bibliographic record
Abstract
INTRODUCTION: The present study assessed the opinion of general practitioners (GPs) concerning their relationships with intensivists. METHODS: An anonymous questionnaire was mailed to 7,239 GPs. GPs were asked about their professional activities, postgraduate intensive care unit (ICU) training, the rate of patient admittance to ICUs, and their relationships with intensivists. Relationship assessment was performed by using a graduated visual analogue scale (VAS) ranging from 0 (dissatisfaction) to 100 (satisfaction). A multivariate analysis with stepwise logistic regression was performed to isolate factors explaining dissatisfaction (VAS score, < 25th percentile). RESULTS: Twenty-two percent of the GPs (1,561) responded. The median satisfaction score was 57 of 100 (interquartile (IQ), 35 to 77]. Five independent factors of dissatisfaction were identified: no information provided to GPs at patient admission (odds ratio (OR) = 2.55 (1.71 to 3.80)); poor quality of family reception in the ICU (OR = 2.06 (1.40 to 3.02)); the ICU's family contact person's identity or function or both is unclear (OR = 1.48 (1.03 to 2.12)), lack of family information (OR = 2.02 (2.48 to 2.75)), and lack of discharge report (OR = 3.39 (1.70 to 6.76)). Three independent factors prevent dissatisfaction: age of GPs <or=45 years (OR = 0.69 (0.51 to 0.94)); the GP is called at patient ICU admission (OR = 0.44 (0.31 to 0.63)); and GP involvement in treatment decisions (OR = 0.17 (0.07 to 0.40)). CONCLUSIONS: Considerable improvement in GP/intensivist relationships can be achieved through increased communication measures.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".