The psychosocial impact of stigma in people with head and neck or lung cancer
Bibliographic record
Abstract
BACKGROUND: Lung and head and neck cancers are widely believed to produce psychologically destructive stigma because they are linked to avoidable risk-producing behaviors and are highly visible, but little research has tested these ideas. We examined cancer-related stigma, its determinants, and its psychosocial impact in lung (n = 107) and head and neck cancer survivors (n = 99) ≤ 3 years post-diagnosis. We investigated cancer site, self-blame, disfigurement, and sex as determinants, benefit finding as a moderator, and illness intrusiveness as a mediator of the relation between stigma and its psychosocial impact. METHODS: Prospective participants received questionnaire packages 2 weeks before scheduled follow-up appointments. They self-administered widely used measures of subjective well-being, distress, stigma, self-blame, disfigurement, illness intrusiveness, and post-traumatic growth. RESULTS: As hypothesized, stigma correlated significantly and uniquely with negative psychosocial impact, but contrary to common beliefs, reported stigma was comparatively low. Reported stigma was higher in (i) men than women, (ii) lung as compared with head and neck cancer, and (iii) people who were highly disfigured by cancer and/or its treatment. Benefit finding buffered stigma's deleterious effects, and illness intrusiveness was a partial mediator of its psychosocial impact. CONCLUSIONS: Stigma exerts a powerful, deleterious psychosocial impact in lung and head and neck cancers, but is less common than believed. Patients should be encouraged to remain involved in valued activities and roles and to use benefit finding to limit its negative effects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".