Job satisfaction of general practitioners: a cross-sectional survey in 34 countries
Bibliographic record
Abstract
BACKGROUND: Job satisfaction of general practitioners (GPs) is important because of the consequences of low satisfaction for GPs, their patients and the health system, such as higher turnover, health problems for the physicians themselves, less satisfied patients, poor clinical outcomes and suboptimal health care delivery. In this study, we aim to explain differences in the job satisfaction of GPs within and between countries. METHODS: We performed a secondary analysis of cross-sectional survey data, collected between 2010 and 2012 on 7379 GPs in 34 (mostly European) countries, as well as data on country and health system characteristics from public databases. Job satisfaction is measured through a composite score of six items about self-reported job experience. Operationalisation of the theoretical constructs includes variables, such as the range of services GPs provide, working hours, employment status, and feedback from colleagues. Data were analysed using linear multilevel regression analysis, with countries and GPs as levels. We developed hypotheses on the basis of the Social Production Function Theory, assuming that GPs 'produce' job satisfaction through stimulating work that provides a certain level of comfort, adds to their social status and provides behavioural confirmation. RESULTS: Job satisfaction varies between GPs and countries, with high satisfaction in Denmark and Canada (on average 2.97 and 2.77 on a scale from 1-4, respectively) and low job satisfaction in Spain (mean 2.15) and Hungary (mean 2.17). One-third of the total variance is situated on the country level, indicating large differences between countries, and countries with a higher GDP per capita have more satisfied GPs. Health system characteristics are not related to GP job satisfaction. At the GP and practice level, performing technical procedures and providing preventive care, feedback from colleagues, and patient satisfaction are positively related to GP job satisfaction and working more hours is negatively related GP job satisfaction. CONCLUSION: Overall and in terms of our theoretical approach, we found that GPs are able to 'produce' work-related well-being through activities and resources related to stimulation, comfort and behavioural confirmation, but not to status.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".