Psychological adjustment and marital satisfaction following head injury. Which critical personal characteristics should both partners develop?
Bibliographic record
Abstract
UNLABELLED: PRIMARY OBJECTIVE AND RESEARCH DESIGN: Using a correlational design, this study verifies the relationships between personal characteristics of individuals with TBI and their spouses and their level of psychological and marital adjustment. METHODS AND PROCEDURE: Seventy individuals with TBI and their spouses in the post-acute rehabilitation phase completed self-report questionnaires assessing the predictive variables (coping and social problem-solving strategies; perceived communication skills) and the criteria variables of psychological and marital adjustment. MAIN OUTCOMES AND RESULTS: In the target group, the characteristics most strongly related to adjustment variables were an effective attitude towards problems, infrequent use of avoidance coping strategies, and a positive perception of one's spouse's communication skills. Individuals with TBI and their spouses report significantly lower scores on some of these personal characteristics, compared to those of a matched control group of 70 couples from the general population. CONCLUSIONS: Specific personal characteristics are critical for psychological and marital adjustment following TBI. This knowledge may be of relevance for detecting couples at risk for developing difficulties in the post-acute rehabilitation phase. Rehabilitation interventions targeting the personal characteristics identified as critical for the adjustment process could help to prevent these difficulties.
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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 | 0.008 |
| 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".