Distress in the workplace: Characterizing the relationship of burnout measures to the Occupational Depression Inventory.
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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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".