The Relationship among Dyadic Adjustment and Disease Burden in Patients with Bipolar Disorder and Their Spouses
Bibliographic record
Abstract
(1) Background: Spouses of individuals with bipolar disorder (BD) experience significant burdens, and the perception of the burden may affect dyadic adjustment. We aimed to investigate the sexual functions, alexithymic traits, marital satisfaction, and burden in patients with BD and their spouses. We also aimed to assess the mediating role of sexual functions and alexithymia in the relationship between burden and dyadic adjustment. (2) Methods: We included 81 patients with BD type 1 (40.69 ± 8.55 years, 65.4% female, and 34.6% male) and their healthy spouses (40.95 ± 7.30 years, 34.6% female, and 65.4% male) and 78 healthy controls (38.90 ± 5.88, 48.7% female, and 51.3% male). The participants were evaluated using the Golombok-Rust Inventory of Sexual Satisfaction (GRISS), Dyadic Adjustment Scale (DAS), Hamilton Depression Rating Scale (HDRS), Toronto Alexithymia Scale-20 (TAS-20), and Burden Assessment Scale (BAS). (3) Results: The GRISS scores of the control group were significantly lower than the spouses and BD groups. The DAS total score of the control group was significantly higher than that of the spouses and BD groups. Regression analyses revealed that TAS, GRISS, and HDRS scores were associated with DAS scores in the BD group. In the spouse group, TAS and BAS scores were associated with DAS scores. The GRISS scores partially mediated the relationship between dyadic adjustment and burden in the spouses of patients with BD. (4) Conclusions: Mental health professionals should regularly scan caregivers' perceptions of burden. Appropriate psychosocial interventions could help spouses of patients with BD to cope better with the burden and improve dyadic adjustment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".