Burnout Syndrome, Mental Splitting and Depression in Female Health Care Professionals
Bibliographic record
Abstract
BACKGROUND The early days of a given experience are associated with typically what might be characterized as an idealized enthusiasm. Conversely burnout syndrome experienced later in the given experience is associated with disillusionment, disappointment, and symptoms which resemble a depression. This very common propensity is a parallel to the concept of "splitting" described by Kernberg with a pronounced "black and white" perceptual dichotomy between the early idealization and later disillusionment. This study intends examination of relationships between burnout syndrome, depression, and Kernberg's concept of splitting. MATERIAL AND METHODS In this present study, we assessed 132 female health care professionals working with a population of diabetic patients utilizing Burnout Measure (BM) Splitting Index (SI), Beck Depression Inventory II (BDI-II), and additional psychometric instruments, the Toronto Alexithymia Scale and the Trauma Symptoms Checklist. RESULTS The study results indicated significant Spearman correlations between burnout syndrome as measured by BM and depression (BDI-II) (R=0.62, P<0.01), and burnout syndrome as measured by BM and splitting (SI) (R=0.45, P<0.01). These findings may have implications for prevention and treatment of burnout syndrome. CONCLUSIONS The current study findings provide implications that the defensive mechanism of splitting may allow for the prediction of burnout symptoms which in turn may allow for the prediction of burnout syndrome. This dynamics may potentially be of use in both the potential detection and prevention of burnout syndrome.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".