Quality of Life and Marital Adjustment in Remitted Psychiatric Illness
Bibliographic record
Abstract
People with mental disorders experience impaired quality of life (QOL). In India, spouses form the most important caregiver for the patient and therefore impact the patients' QOL. However, relatively little is known about marital adjustment, which can definitely influence QOL of patients with mental illness. This study intended to explore marital adjustment and QOL among remitted patients with schizophrenia (SC), depression, and bipolar disorders (BPADs) and to study differences, if any, between the groups. Using a cross-sectional design, consecutive patients (N = 150) with an ICD-10-Diagnostic Criteria for Research diagnosis of SC, depression (recurrent depressive disorder [RDD]), or BPAD, who were currently in remission, were taken up for the study and administered the WHOQOL-BREF for assessing QOL and the Marital Adjustment Inventory for assessing marital adjustment, separately for the husband and the wife. The patients with SC reported poor QOL, whereas a better QOL was seen in those with BPAD and RDD, with significant differences noted between all three groups (p < 0.001). Marital adjustment was perceived to be poor by the patients but not so by the spouses. The greatest marital dissatisfaction was reported by the patients with SC (96%). A positive correlation was observed between the patients' perception of marital adjustment and QOL (p < 0.05). Provision of mental health care should take into consideration patients' possible perception of marital maladjustment and factor these into treatment strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".