Complementary and Conventional Medicine in Switzerland: Comparing Characteristics of General Practitioners
Bibliographic record
Abstract
OBJECTIVES: Do structural characteristics of general practitioners (GPs) who practice complementary medicine (CAM) differ from those GPs who do not? Assessed characteristics included experience and professional integration of general practitioners (GPs), workload, medical activities, and personal and technical resources of practices. The investigated CAM disciplines were anthroposophic medicine, homoeopathy, traditional Chinese medicine, neural therapy and herbal medicine. MATERIAL AND METHODS: We designed a cross-sectional study with convenience and stratified samples of GPs providing conventional (COM) and/or complementary primary care in Switzerland. The samples were taken from the database of the Swiss medical association (FMH) and from CAM societies. Data were collected using a postal questionnaire. RESULTS: Of the 650 practitioners who were included in the study, 191 were COM, 167 noncertified CAM and 292 certified CAM physicians. The proportion of females was higher in the population of CAM physicians. Gender-adjusted age did not differ between CAM and COM physicians. Nearly twice as many CAM physicians work part-time. Differences were also seen for the majority of structural characteristics such as qualification of physicians, type of practice, type of staff, and presence of technical equipment. CONCLUSION: The study results show that structural characteristics of primary health care do differ between CAM and COM practitioners. We assumed that the activities of GPs are defined essentially by analyzed structures. The results are to be considered for evaluations in primary health care, particularly when quality of health care is assessed.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".