Spanish Burnout Inventory (SBI) Validation among University Professors During COVID-19
Bibliographic record
Abstract
The objective was to evaluate the evidence of the factorial structure of the CESQT in Mexican University Professors through their response patterns during the first peak of the COVID-19 Pandemic. The method considers the psychometric properties of the instrument by examining a sample of n=600 of Mexican University Professors. The scale featured a sociodemographic section and the CESQT questionnaire by Gil-Monte (2005). The inventory has 20 items grouped into 4 dimensions related to: Enthusiasm for work, Emotional Fatigue, Indolence and Guilt. Factor analysis, variance and covariance were performed using the maximum likelihood method with AMOS24®. The results of the study demonstrated that the instrument is valid and reliable to measure Burnout levels in teachers and significant differences were found with the Gil-Monte results. Cronbach's Alpha Coefficient was greater than 0.70 for the four scales of the instrument. The original value of this study contributes to the development of the body of knowledge about the scenario perceived by University Professors during the first peak of the COVID-19 Pandemic, about valid instruments to measure Burnout Syndrome in Spanish-speaking countries. It is concluded that the results provide evidence of the psychometric properties of the CESQT during the study of Burnout Syndrome in the Mexican cultural context, the first peak of the COVID-19 Pandemic. All the measurement scales satisfy the criteria of validity and reliability. The factorial analysis of the Theoretical model of each one of the dimensions of Gil-Monte in an empirical way.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".