Alexithymia as a risk factor for poor emotional outcomes in adults with acquired brain injury
Bibliographic record
Abstract
Emotional disorders are pervasive in the acquired brain injury (ABI) population, adversely affecting quality of life and rehabilitation. This study aimed to explore the unique associative effects of alexithymia as measured by the Perth Alexithymia Questionnaire (PAQ; i.e., difficulty identifying positive/negative feelings, difficulty describing positive/negative feelings, and externally orientated thinking), on emotional outcomes as measured by the Depression Anxiety Stress Scale-21 (DASS-21) and Mayo-Portland Adaptability Inventory (MPAI-4) Adjustment index, in 83 adults with ABI. The addition of alexithymia to hierarchical multiple regression models (controlling for demographic, injury-related, and functional outcome variables) yielded statistically significant changes in R2 for all emotional outcome measures (i.e., Depression, Anxiety, Stress, and Adjustment). Difficulty identifying negative feelings was found to be a significant unique predictor of Depression (β = .43 p = <.001), Anxiety (β = .40, p <.001), Stress (β = .49, p <.001), and Adjustment (β = .26, p = .001). Externally oriented thinking was found to be a significant unique predictor of Adjustment (β = -.15, p = .033). These findings strengthen the argument that alexithymia, especially difficulties identifying negative feelings, may be an important risk factor for psychological distress in ABI and should be considered during early rehabilitation.
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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 | 0.001 |
| 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".