A Multidimensional Questionnaire to Measure Career Satisfaction of Physicians: Validation of the Polish Version of the 4CornerSAT
Bibliographic record
Abstract
To study physicians' satisfaction with a multidimensional approach, the 4CornerSAT questionnaire to measure the career satisfaction of physicians was conceptualized in English and later adapted into Polish. In this study, we aimed to test the reliability and validity of the adapted 4CornerSAT questionnaire in Poland and confirm its the tetra-dimensional structure. In 2018, physicians working in 15 Polish hospitals were invited to participate in a survey that included the Polish 4CornerSAT. We evaluated the questionnaire's reliability by computing Cronbach's alpha coefficients. We also computed a Pearson correlation coefficient between the reported global item of satisfaction and the standardized level of career satisfaction. A confirmatory factorial analysis (CFA) tested the tetra-dimensional structure of the questionnaire in Polish. In total, 1003 physicians participated in this study. The questionnaire's internal consistency and concurrent validity were optimal. In the CFA, good model fit indicators were observed. In conclusion, the Polish version of the 4CornerSAT demonstrated good psychometric properties. The adapted questionnaire has evidence of its validity and reliability in Poland to be used in further studies and to monitor physicians' wellness as a health care system indicator. Our approach to adapt and validate this questionnaire could be replicated in other settings.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".