Factors related to consultation time: Experience in Slovenia
Bibliographic record
Abstract
OBJECTIVE: Consultation time has a serious impact on physicians' work and patient satisfaction. No systematic study of consultation time in general practice in Slovenia has yet been carried out. The aim of the present study was to measure consultation time, to identify the factors influencing it, and to study the influence of the workload of general practitioners on consultation time. DESIGN: A total of 42 general practitioners participated in this cross-sectional study. Each physician collected data from 300 consecutive consultations and measured the length of the visit. SETTING: Forty-two randomly selected general practices in Slovenia. SUBJECTS: Patients of 42 general practices. MAIN OUTCOME MEASURES: Average consultation time in general practice in Slovenia; factors influencing consultation time in Slovenia. RESULTS: Data from 12 501 visits to the surgery were collected. A quarter of all visits (25.5%) were administrative. The mean consultation time was 6.9 minutes (median 6.0 minutes, 5%-95% interval: 1.0-16.0 minutes). Longer consultation time was predicted by: patient-related factors (female gender, higher age, higher level of education, higher number of health problems, change of physician within the last year), physician-related factors (higher age), physicians' workload (absence of high workload), and the type of visit (consultation and/or clinical examination). CONCLUSION: Consultation time in general practice is short, and depends on the characteristics of the patient and the physician, the physician's workload, and the type of visit. A reduction of high workload in general practice should be one of the priorities of the healthcare system.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".