The burden of stress in head and neck cancer
Bibliographic record
Abstract
BACKGROUND: Head and neck cancer (HNCa) introduces numerous stressors. We developed the Cancer-Related Stressors Checklist (CRSC), which documents exposure to seven categories of common stressors and emotional distress. We surveyed HNCa survivors and examined associations among exposure to cancer-related stressors, illness intrusiveness (i.e., cancer-induced interference with lifestyles, activities, and interests), and distress. We also investigated whether reported exposure rates differ between self-administered and interviewer-administered measures. METHODS: Respondents included HNCa survivors, stratified by sex, who participated in one of two clinical studies (N1 = 162; N2 = 408) examining the psychosocial impact of illness intrusiveness. All completed the CRSC, the Center for Epidemiologic Studies Depression Scale, and the Illness Intrusiveness Ratings Scale. Study 1 respondents self-administered the instruments; an interviewer administered them in Study 2. We gathered clinical data by self-report and from medical records. RESULTS: High inter-rater reliability corroborated the 8-subscale structure of the CRSC (Krippendorff alpha = .92). Cancer-related stressor exposures differed significantly across categories (interpersonal stressors were most common). Controlling for empirically identified covariates and distress, exposure to each cancer-related stressor correlated significantly and uniquely with illness intrusiveness. All stressor categories correlated significantly with distress, but coefficients were low to moderate, substantiating incremental validity. Respondents reported fewer exposures when materials were self-administered as compared with interviewer-administered, but reported distress levels did not differ by mode of administration. CONCLUSIONS: Cancer-related stressors are common and burdensome in HNCa and, therefore, merit clinical attention. Identifying specific stressors will allow more targeted and effective interventions to alleviate and prevent distress.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".