Validation of the Maslach Burnout Inventory-General Survey 9-item short version: psychometric properties and measurement invariance across age, gender, and continent
Bibliographic record
Abstract
Background: The Maslach Burnout Inventory-General Survey (MBI-GS) stands as the preeminent tool for assessing burnout across various professions. Although the MBI-GS9 emerged as a derivative of the MBI-GS and has seen extensive use over several years, a comprehensive examination of its psychometric properties has yet to be undertaken. Methods: This study followed the Standards for Educational and Psychological Testing guidelines to validate the MBI-GS9. Employing a combined approach of classical test theory and item response theory, particularly Rasch analysis, within an integrated framework, the study analyzed data from 16,132 participants gathered between 2005 and 2015 by the Centre for Organizational Research at Acadia University. Results: The findings revealed that the MBI-GS9 exhibited satisfactory reliability and validity akin to its predecessor, the MBI-GS. Across its three dimensions, Cronbach's α and omega coefficients ranged from 0.84 to 0.91. Notably, the MBI-GS9 displayed no floor/ceiling effects and demonstrated good item fit, ordered threshold, acceptable person and item separation and reliability, clear item difficulty hierarchy, and a well-distributed item threshold. However, the results suggested a recommended minimum sample size of 350 to mitigate potential information loss when employing the MBI-GS9. Beyond this threshold, the observed mean difference between the MBI-GS and MBI-GS9 held minimal practical significance. Furthermore, measurement equivalence tests indicated that the MBI-GS9 maintained an equivalent three-factor structure and factor loadings across various gender, age, and continent groups, albeit with inequivalent latent values across continents. Conclusion: In sum, the MBI-GS9 emerges as a reliable and valid alternative to the MBI-GS, particularly when utilized within large, diverse samples across different age and gender demographics. However, to address potential information loss, a substantial sample size is recommended when employing the MBI-GS9. In addition, for cross-cultural comparisons, it is imperative to initially assess equivalence across different language versions at both the item and scale levels.
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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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; 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".