Personal Health Practices and Patient Counseling of German Physicians in Private Practice
Bibliographic record
Abstract
We examined physicians' personal health behaviors and the influence on their patient counseling practices in a representative sample ( N=414 ) of physicians in private practice in Schleswig-Holstein, Germany. Physicians reported significantly better physical but poorer mental health compared to the general population (GP; P>0.01 ). The majority presented with normal weight (47.9% male, 73.1% female physicians versus 24.5/41.0% GP) or overweight (47.5% male, 20.0% female versus 52.9/35.6% GP). Frequency of exercise and fruit and vegetable consumption was higher than in the GP. About 70% drank coffee or tea more than once a day, but only 13.2% of female and 21.8% of male physicians were current smokers (GP 20.1/30.5%). More than half (56.1%) usually or always counseled a typical patient on exercise versus nutrition (47.0%), weight (45.8%), smoking (39.9%), and alcohol (30.0%). Doctors with better personal exercise, nutrition, smoking, and alcohol behaviors counseled their patients significantly more often on related topics. Despite better physical health and health behaviors in these German doctors compared to the GP, there is room for improvement (smoking, overweight), which could be expected to positively influence the counseling practice and impact of doctors' role modeling on patients.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".