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 ( χ 2 = 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".