Validation of the Maslach burnout inventory-general survey 9-item short version (MBI-GS9) among care aides in Canadian nursing homes
Bibliographic record
Abstract
BACKGROUND: The Maslach Burnout Inventory-General Survey (MBI-GS) is the leading measure of burnout for all occupations. The MBI-GS9, the 9-item version of the MBI-GS, was formulated based on the MBI-GS and has been used for several years. However, very few studies have systematically tested its psychometric properties, and none have focused on care aides working in nursing homes who are susceptible to burnout. METHODS: Following the Standards for Educational and Psychological Testing, this study validated the MBI-GS9 among 3,765 care aides from 91 Canadian nursing homes, using data collected between September 2019 and February 2020 by the Translating Research in Elder Care (TREC) program. RESULTS: The Exhaustion subscale had good reliability with coefficients around 0.66-0.74. The Cynicism subscale had medium reliability with coefficients around 0.60-0.66, and the Efficacy subscale also had medium reliability with coefficients around 0.51-0.58. The MBI-GS9 was significantly correlated with various conceptually related constructs, such as health status, working environment, job satisfaction, psychological empowerment, work engagement, and organizational citizenship behaviors. The MBI-GS9 had a three-factor structure in the full sample and showed equivalent factor structure, factor loadings, latent values, factor variance and error variance across different sex and age groups. Care aides with English as their first language showed higher latent values of the Exhaustion subscale compared to those with English as a second language. CONCLUSION: Overall, the MBI-GS9 exhibited acceptable psychometric properties, but medium reliability of cynicism and efficacy subscales, for measuring burnout among care aides in nursing homes, demonstrating equivalence across sex and gender groups. When comparing across different languages or racial or ethnic groups among care aides, it is important to consider inequivalent latent values on Exhaustion before comparing scores on the measure.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".