Correlates of Quality of Life Among African American and White Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: African Americans continue to suffer disproportionately from cancer morbidity and mortality, with emerging evidence suggesting potential quality of life (QOL) disparities in the survivorship period. OBJECTIVE: The objective of the study was to assess sociodemographic, clinical, and psychosocial factors associated with physical and mental health QOL (PHQOL and MHQOL) among African American and white cancer survivors. METHODS: Patients were recruited from tumor registries. Telephone interviews were conducted with 248 African American and 244 white respondents with a history of breast, prostate, or colorectal cancers. Multivariate regression models were used to assess what factors were associated with PHQOL and MHQOL. RESULTS: Key racial differences in adjusted analyses included poorer MHQOL scores among African Americans compared with white survivors. Furthermore, race moderated the relationship between perceived social support and MHQOL, where higher social support levels were associated with increased MHQOL among African Americans. Other correlates of QOL impacted racial groups similarly. For example, factors associated with PHQOL scores included being unemployed, being uninsured, the presence of medical comorbidities, a longer time since diagnosis, and higher levels of cancer-related stress appraisals. Factors associated with MHQOL scores included being unemployed, higher levels of daily stress, higher levels of stress associated with the diagnosis, higher levels of education, higher levels of perceived social support, and higher levels of spirituality. CONCLUSION: Interventions aimed at increasing social support may have important implications for improving QOL outcomes among African Americans. IMPLICATIONS FOR PRACTICE: Measuring and understanding factors associated with QOL have important implications for patient adjustment and clinical decision making.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".