Sociocultural determinants of psychological distress and coping among South Asian individuals with chronic illness
Bibliographic record
Abstract
Abstract Sociocultural determinants affect the physical and mental well-being and coping of individuals with chronic illnesses. However, no studies have examined the relationship of sociocultural determinants with psychological distress and coping. The purpose of this study was to determine the levels of psychological distress and coping among individuals with chronic illness and identify sociocultural determinants affecting distress and coping. A cross-sectional design was used. Data were collected from a convenience sample of 384 individuals admitted to inpatient settings and attending outpatient clinics at two tertiary care hospitals. The validated Urdu-translated version of the Hospital Anxiety and Depression Scale, and the Brief COPE scales were used for data collection. The mean HADS-Depression score was (11.38 ± 2.53), and the HADS-Anxiety score was 13.42 ± 2.34), indicating high levels of depression and anxiety. The most commonly used coping strategies were problem-focused coping (15.95 ± 4.95), followed by emotion-focused coping (15.01 ± 2.33), and avoidance coping (13.89 ± 4.77). Higher levels of psychological distress in individuals with chronic illnesses underscore the importance of implementing community-based support approaches. Varied use of coping strategies was influenced by the type of chronic illness, living conditions, educational level, years of living with a chronic illness, family dynamics, and available support systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".