High prevalence of burnout syndrome in Czech general practitioners: A cross-sectional survey
Bibliographic record
Abstract
Objective: A wide range in prevalence rates of burnout among general practitioners (GPs) has been reported in various regions, with an increasing trend. This nationwide cross-sectional study aimed to estimate the prevalence and associated determinants of burnout in Czech GPs. Methods: 1000 randomly selected physicians from the Czech Society of General Practitioners (through a pseudorandom number generator) were emailed an online survey based on the Maslach Burnout Inventory - Human Services Survey. Data collection was performed between January and February 2023. Results: 331 questionnaires were obtained (227 females and 104 males, mean age - 49.9 years, the mean number of registered patients - 1951). 21.8 % of GPs scored a high level of burnout in all three of its dimensions and 23.9 % in no dimension at all. The most prevalent dimension was reduced personal accomplishment (PA, 56.2 %) followed by emotional exhaustion (EE, 50.2 %) and depersonalization (DP, 40.5 %). Reaching burnout in all three dimensions was significantly more frequent in males and in GPs registering a number of patients above the median. Increasing age and years of practice were protective factors for DP but risk factors for reduced PA. Employed GPs had lower EE scores than GP practice owners. The respondents' basic characteristics reflected their presence among Czech GPs, which testifies against selection bias. Conclusions: The high rate of burnout (∼22 %) should be addressed by promoting personal resources along with the perception of the importance of GPs in society. A sufficiently dense network of GPs should allow them to register a lower number of patients.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".