Protective Skin Care Behaviors in Cancer Survivors
Bibliographic record
Abstract
PURPOSE: Research suggests that physicians neglect preventive care for cancer survivors. A survivor's self-motivation with respect to preventive care is unknown. Using protective skin care as a proxy, our aims were to characterize preventive care in cancer survivors and to identify factors associated with appropriate prevention. METHODS: Using data from the 2009 U.S. Health Information National Trends Survey, we compared preventive skin care patterns in cancer survivors and non-cancer patients. Primary endpoints were the use of sunscreens, long-sleeved shirts, hats, and shade. RESULTS: We identified 179 early cancer survivors (<5 years), 242 intermediate cancer survivors (5-10 years), 412 long-term cancer survivors (>10 years), and 5951 non-cancer patients. The use of sunscreens (60%), long-sleeved shirts (88%), hats (58%), and shade (68%) was suboptimal. Overall, cancer survivors were not more likely to adhere to preventive care (p = 0.89). A composite score showed a significant difference between the cancer survivor groups (p < 0.01) whereby intermediate survivors reported the best preventive practices. CONCLUSIONS: A prior diagnosis of cancer does not appear to increase personal compliance with cancer prevention. Reasons for this poor engagement are not clear. Targeted strategies to increase self-motivation might improve preventive practices in cancer 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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".