Factors Associated with Satisfaction of Hospital Physicians: A Systematic Review on European Data
Bibliographic record
Abstract
BACKGROUND: Physician satisfaction is a multidimensional concept related to many factors. Despite the wide range of research regarding factors affecting physician satisfaction in different European countries, there is a lack of literature reviews analyzing and summarizing current evidence. The aim of the article is to synthetize the literature studying the factors associated with physician satisfaction. METHODS: We searched: MEDLINE, Embase, PsycINFO, CINAHL and the Cochrane Library from January 2000 to January 2017. The eligibility criteria included: (1) target population: physicians working in European hospitals; (2) quantitative research aimed at assessing physician satisfaction and associated factors; (3) use of validated tools. We performed a narrative synthesis. RESULTS: After screening 8585 records, 368 full text articles were independently checked and finally 24 studies were included for qualitative analysis. The included studies surveyed 20,000 doctors from 12 European countries. The tools and scales used in the analyzed research to measure physician satisfaction varied to a large extent. We extracted all pre-specified factors, reported as statistically significant/non-significant. Analyzed factors were divided into three groups: personal, intrinsic and contextual factors. The majority of factors are modifiable and positively associated with characteristics of contextual factors, such as work-place setting/work environment. In the group of work-place related factors, quality of management/leadership, opportunity for professional development and colleague support have been deemed statistically significant in numerous studies. CONCLUSIONS: We identified more studies appraising the effect of contextual factors (such as work environment, work-place characteristics), highlighting a positive association between those factors and physician satisfaction, compared with personal and intrinsic factors. Numerous studies confirmed statistically significant associations between physician satisfaction and quality of management, professional development and colleague support/team climate. Due to the health workforce crisis, knowledge regarding physician satisfaction and associated factors is essential to healthcare managers and policy makers for more stable human resources management.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".