Distress in the workplace: Characterizing the relationship of burnout measures to the Occupational Depression Inventory.
Why this work is in the frame
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Bibliographic record
Abstract
Burnout has been found to problematically overlap with depression. However, the generalizability of this finding remains disputed. This study examined burnout–depression overlap using the recently developed Occupational Depression Inventory (ODI) and two burnout measures, the Maslach Burnout Inventory (MBI) and the Copenhagen Burnout Inventory (CBI). The study involved two teacher samples employed in France (N = 1,450) and New Zealand (N = 492). We found the correlations of the ODI with (a) the MBI’s emotional exhaustion (EE) subscale and (b) the CBI to reach .80. An explanation of these high correlations based on content overlap in fatigue-related items was ruled out. The ODI–EE and ODI–CBI correlations were significantly stronger than the correlations among the MBI’s subscales. Exploratory structural equation modeling bifactor analyses revealed that the ODI captures what the MBI’s EE subscale and the CBI measure. The general factor explained 86% of the common variance extracted when considering ODI and EE items and 89% when considering ODI and CBI items. The findings indicate that burnout’s exhaustion core is part of a depressive syndrome. Importantly, the ODI not only assesses exhaustion but also each of the other core symptoms of major depression, including suicidal thoughts. In contrast to burnout measures, the ODI allows for both a dimensional and a diagnostic approach to job-related distress, consistent with the history of clinical research on depression. Moreover, the ODI has demonstrated particularly robust psychometric and structural properties in past research. The ODI’s value for occupational medical specialists in replacing burnout measures is discussed.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it