Do stigma and its psychosocial impact differ between Asian-born Chinese immigrants and Western-born Caucasians with head and neck cancer?
Bibliographic record
Abstract
Stigma appears to influence emotional distress and well-being in cancer survivors, but cross-cultural differences have been ignored. Previous studies suggest that stigma may be especially relevant for survivors of Asian origin. However, their study designs (e.g. focused on female cancers, qualitative designs, and an absence of comparison groups) limit the strength of this conclusion. We hypothesized that (1) Asian-born Chinese immigrants (AI) would report more perceived cancer-related stigma than Western-born Caucasians (WBC); and (2) the impact of stigma on emotional distress and well-being would be greater in AI as compared to WBC. Head and neck cancer survivors (n = 118 AI and n = 404 WBC) completed measures of well-being, emotional distress, and a three-item indicator of stigma in structured interviews. The majority of respondents (59%) reported one or more indicators of stigma. Stigma correlated significantly with emotional distress (r = .13, p = .004) and well-being (r = -.09, p = .032). Contrary to our hypotheses, WBCs and AIs did not differ in reported stigma nor did we detect differences in its psychosocial impact. Stigma exerts a deleterious psychosocial impact on head and neck cancer survivors. It did not differ significantly between AI and WBC survivors.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".