The impact of alexithymia on burnout amongst relatives of people who suffer from traumatic brain injury
Bibliographic record
Abstract
BACKGROUND: Although previous research has provided some indication of the association of alexithymia and burnout, this is the first study exploring the association of these two variables in a sample of relatives of individuals who have sustained traumatic brain injury (TBI). OBJECTIVE: To explore the degree to which relatives experience burnout and the extent to which alexithymia acts as a pre-disposing factor, controlling for depression and coping strategies. METHOD: Toronto Alexithymia Scale-20, Maslach Burnout Inventory-Human Services, Estonian COPE Dispositional Inventory and Beck Depression Inventory-II were completed by 60 relatives of patients with TBI drawn from a tertiary head injury clinic population. RESULTS: Levels of emotional exhaustion, reduced personal accomplishment and depression were significantly higher in the sub-group of relatives with alexithymia than in the sub-group of relatives without alexithymia. Difficulty describing feelings and externally oriented thinking style were significant predictors of emotional exhaustion, while difficulty identifying feelings and difficulty describing feelings were important predictors of depersonalization. CONCLUSIONS: Relatives who present with alexithymia need to be identified at an early stage to minimize risks of burnout leading to adverse effects on patient-caregiver relationships.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".