Alexithymia and Avoidance Coping Following Traumatic Brain Injury
Bibliographic record
Abstract
BACKGROUND: Individuals who develop maladaptive coping styles after traumatic brain injury (TBI) usually experience difficulty expressing their emotional state, increasing the risk of psychological distress. Difficulties expressing emotion and identifying feelings are features of alexithymia, which is prevalent following TBI. OBJECTIVE: To examine the relations among coping styles, alexithymia, and psychological distress following TBI. PARTICIPANTS: Seventy-one patients with TBI drawn from a head injury clinic population and 54 demographically matched healthy controls. MAIN MEASURES: Toronto Alexithymia Scale-20, Estonian COPE-D Inventory, Beck Depression Inventory-II, and Beck Anxiety Inventory. RESULTS: The participants with TBI exhibited significantly higher rates of alexithymia and psychological distress and lower levels of task-oriented coping than healthy controls. Levels of avoidance coping and psychological distress were significantly higher in a subgroup of TBI patients with alexithymia than in a non-alexithymic TBI subsample. There were significant relations among alexithymia, avoidance coping, and levels of psychological distress. Regression analysis revealed that difficulty identifying feelings was a significant predictor for psychological distress. CONCLUSION: Early screening for alexithymia following TBI might identify those most at risk of developing maladaptive coping mechanisms. This could assist in developing early rehabilitation interventions to reduce vulnerability to later psychological distress.
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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 | 0.000 | 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) | 0.002 | 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".