Emotional distress in cancer survivors from various ethnic backgrounds: Analysis of the multi‐ethnic HELIUS study
Bibliographic record
Abstract
PURPOSE: Insight into emotional distress of cancer survivors from ethnic minority groups in Europe is scarce. We aimed to compare distress levels of survivors from ethnic minorities to that of the majority population, determine whether the association between having cancer (yes vs. no) and distress differs among ethnic groups and investigate sociocultural correlates of distress. METHODS: Cross-sectional data were derived from HELIUS, a multi-ethnic cohort study conducted in the Netherlands. Of 19,147 participants, 351 were diagnosed with cancer (n = 130 Dutch, n = 75 African Surinamese, n = 53 South-Asian Surinamese, n = 43 Moroccan, n = 28 Turkish, n = 22 Ghanaian). Distress (PHQ-9, MCS-12) and correlates were assessed by self-report. Cancer-related variables were derived from the Netherlands Cancer Registry. RESULTS: : 0.44-1.17; adjusted models). The association between having cancer or not with distress differed in direction between Dutch and the non-Dutch ethnic groups: Non-Dutch cancer patients tended to have more distress than their cancer-free peers, whereas Dutch cancer patients tended to have less distress than their cancer-free peers. For Moroccan and Turkish patients, the acculturation style of separation/marginalization, compared to integration/assimilation, was associated with higher depressive symptoms. In analyses pooling data from all ethnic minorities, lower health literacy, lower emotional support satisfaction and younger age at the time of migration were associated with higher depressive symptoms. Lower health literacy, fewer emotional support transactions, and more frequent attendance at religious services were associated with worse mental health. CONCLUSION: Cancer survivors from ethnic minorities experience more distress than those from the majority population. Culturally sensitive supportive care should be considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".