Negative cancer stereotypes and disease‐specific self‐concept in head and neck cancer
Bibliographic record
Abstract
BACKGROUND: Life-threatening diseases, such as head and neck cancer (HNCa), can stimulate the emergence of a new disease-specific self-concept. We hypothesized that (i) negative cancer-stereotypes invoke distancing, which inhibits the adoption of a disease-specific self-concept and (ii) patient characteristics, disease and treatment factors, and cancer-related stressors moderate the phenomenon. METHODS: Head and neck cancer outpatients (N = 522) completed a semantic-differential measure of disease-specific self-concept (perceived similarity to the 'cancer patient') and other self-report measures in structured interviews. Negative cancer-stereotypes were represented by the number of semantic-differential dimensions (0-3) along which respondents evaluated the stereotypic 'cancer patient' negatively (i.e., negative valence). We tested the two-way interactions between negative valence and hypothesized moderator variables. RESULTS: We observed significant negative valence × moderator interactions for the following: (i) patient characteristics (education, employment, social networks); (ii) disease and treatment factors (cancer-symptom burden); and (iii) cancer-related stressors (uncertainty, lack of information, and existential threats). Negative cancer stereotypes were consistently associated with distancing of self from the stereotypic 'cancer patient,' but the effect varied across moderator variables. All significant moderators (except employment and social networks) were associated with increasing perceived similarity to the 'cancer patient' when respondents maintained negative stereotypes; perceived similarity decreased when people were employed or had extensive social networks. Moderator effects were less pronounced when respondents did not endorse negative cancer stereotypes. DISCUSSION: When they hold negative stereotypes, people with HNCa distance themselves from a 'cancer patient' identity to preserve self-esteem or social status, but exposure to cancer-related stressors and adaptive demands may attenuate these 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".