The burnout-depression conundrum: investigating construct-relevant multidimensionality across four countries and four patient samples
Bibliographic record
Abstract
This research seeks to contribute to the ongoing discussion about the distinctive nature of burnout and depression. In a first study, we relied on employee samples from four European countries (N = 5199; 51.27% women; Mage = 43.14). In a second study, we relied on a large sample of patients (N = 5791; 53.70% women; Mage = 39.54) who received a diagnosis of burnout, depressive episode, job strain, or adaptation disorder. Across all samples and subsamples, we relied on the bifactor exploratory structural equation modelling to achieve an optimal disaggregation of the variance shared across our measures of burnout and depression from the variance uniquely associated with each specific subscale included in these measures. Our results supported the value of this representation of participants’ responses, as well as their invariance across samples. More precisely, our results revealed a strong underlying global factor representing participants’ levels of psychological distress, as well as the presence of equally strong specific factors supporting the distinctive nature of burnout and depression. This means that, although both conditions share common ground (i.e. psychological distress), they are not redundant. Interestingly, our results also unexpectedly suggested that suicidal ideation might represent a distinctive core component of depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".