Adaptación y validación al español del cuestionario 4CornerSAT para la medida de la satisfacción profesional del personal médico de atención especializada
Bibliographic record
Abstract
BACKGROUND: Satisfaction of physicians is a concern in the healthcare sector, and it requires a multi-dimensional questionnaire in Spanish which studies their high-order needs. The objectives of this study are to adapt the 4CornerSAT Questionnaire to measure career satisfaction of physicians and to evaluate its validity in our context. METHOD: The 4CornerSAT Questionnaire was adapted into Spanish, validating it among physicians of hospitals in Andalusia, Spain. A confirmatory factor analysis (CFA) was performed to corroborate the a priori model, and it was evaluated the internal consistency and the construct validity through the Cronbach's alpha and the correlation between the scale and the global item, respectively. RESULTS: The adapted questionnaire was administrated to 121 specialist physicians. The CFA corroborated the four dimensions of the questionnaire (χ2=114.64, df=94, p<0.07; χ2/df=1.22; RMSEA=0.04). The internal consistency obtained an α=0.92 and the correlation between the summed scale and the global item verified the construct validity (r=0.77; p<0.001). CONCLUSIONS: The 4CornerSAT questionnaire was adapted to Spanish, identifying an adequate construct validity and internal consistency.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".