Nonmelanoma Skin Cancer: Disease-Specific Quality-of-Life Concerns and Distress
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To provide a better understanding of the disease-specific quality-of-life (QOL) concerns of patients with nonmelanoma skin cancer (NMSC). DESIGN: Cross-sectional. SETTING: Skin cancer clinic of Jewish General Hospital in Montreal, Quebec, Canada. SAMPLE: 56 patients with basal cell carcinoma and/or squamous cell carcinoma. METHODS: Descriptive and inferential statistics applied to quantitative self-report data. MAIN RESEARCH VARIABLES: Importance of appearance, psychological distress, and QOL. FINDINGS: The most prevalent concerns included worries about tumor recurrence, as well as the potential size and conspicuousness of the scar. Skin cancer-specific QOL concerns significantly predicted distress manifested through anxious and depressive symptomology. In addition, the social concerns related to the disease were the most significant predictor of distress. CONCLUSIONS: The findings of this study provide healthcare professionals with a broad picture of the most prevalent NMSC-specific concerns, as well as the concerns that are of particular importance for different subgroups of patients. IMPLICATIONS FOR NURSING: Nurses are in a position to provide pivotal psychosocial and informational support to patients, so they need to be aware of the often-overlooked psychosocial effects of NMSC to address these issues and provide optimal care.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".