Coping among South Asian individuals living with chronic illnesses: A latent profile analysis
Bibliographic record
Abstract
Individuals' coping differs based on sociocultural determinants and the nature of illness. This study developed a coping typology for South Asians with chronic illnesses and differentiated the coping profiles based on sociocultural determinants. Individuals (n = 384) with chronic illness were recruited. The Brief COPE scale was used for data collection and latent profile analysis for typology development. The class differences were examined in terms of age, gender, socioeconomic status, education, type of family, smoking, primary decision maker in the family, type of community, number of years living with chronic illness and type of health care services used. Latent profile analysis supported four class model: Avoider‐Emotion (n = 34, 9%), Problem‐Emotion (n = 128, 33.9%), Problem‐Avoider (n = 55, 14.6%) and Emotion‐Avoider (n = 161, 42.6%) copers. Comparison of classes across chronic illness showed that individuals with chronic respiratory disorders were Emotion‐Avoider and Avoider‐Emotion copers, those with cardiac problems were Problem‐Emotional and Problem‐Avoiders copers, those with renal problems were Emotional‐Avoiders and Problem‐Emotions copers, and individuals with mental health issues were mainly Problem‐Emotional and Emotion‐Avoider copers. These class differences were statistically different (χ2 = 134, df = 18, p < .001). The findings can be useful for developing coping programmes for South Asian populations in low‐ and middle‐income countries and South Asian immigrants.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".