What Do Immigrants From Various Cultures Think Is the Best Way to Cope With Depression? Introducing the Cross-Cultural Coping Inventory
Bibliographic record
Abstract
The aim of this study is to introduce a domain-specific instrument, the Cross-Cultural Depression Coping Inventory (CCD-CI), to assess ways in which people from different cultures prefer to cope with depression. Part 1 of this paper describes the development of CCD-CI. A combined etic and emic approach in generating items was used. Principal component analysis on data from a heterogeneous sample of immigrants (N=458) supported a three-factor solution labeled: Engagement, disengagement, and spiritual coping. In Part 2 confirmatory factor analysis were conducted to test if the factors replicated in a mixed ethnic sample of immigrants from Russia (n = 164), Poland (n = 127), Pakistan (n = 128), Somalia (n = 114), and Norwegian students (n = 248). The three-factor model fits the data well and differentiated between the ethnic groups. Most significantly, Somali followed by the Pakistani immigrants scored higher on disengagement and spiritual coping. Inspection of item-level differences showed the largest ethnic variations in coping behavior of communal or social nature. The CCD-CI factors were validated against the Vancouver Index of Acculturation. Adoption to majority culture correlated positively with engagement and negatively with spiritual Coping. Maintenance of origin culture was positively associated with both spiritual coping and disengagement. In Part 3 the construct validity of the CCD-CI was tested in relation to the Brief Cope. The magnitude of the correlations was small to moderate. Taken together results indicate that CCD-CI is a reliable and valid measure of coping strategies related to depression, suitable for adults from different ethnic groups. Implications for research and clinical practice are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".