The Impact of Alexithymia on Relationship Quality and Satisfaction Following Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVES: To explore relationship quality and satisfaction in couples following traumatic brain injury (TBI), obtaining the perspective of both patients with TBI and their noninjured partner; to examine the impact of alexithymia on ratings of relationship quality and satisfaction following TBI. PARTICIPANTS: A total of 47 patients with TBI and their noninjured partners. MEASURES: Twenty-item Toronto Alexithymia Scale; Index of Marital Satisfaction; and Dyadic Adjustment Scale. RESULTS: Significant differences emerged between patients with TBI and their noninjured partners on measures of relationship quality and satisfaction, with patients reporting significantly fewer relationship problems and greater levels of overall relationship satisfaction. In contrast, the noninjured partners of patients with TBI and acquired alexithymia reported significantly lower ratings of overall relationship quality, adjustment, consensus, and cohesion. CONCLUSION: The presence of alexithymia in persons with TBI is an important factor affecting relationship quality and satisfaction. Future research should continue to explore the impact of emotional deficit disorders following TBI on the quality of relationships following injury and, in turn, the risk of relationship breakdown.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".