Optimism, social support, and mental health outcomes in patients with advanced cancer
Bibliographic record
Abstract
OBJECTIVE: Optimism and social support serve as protective factors against distress in medically ill patients. Very few studies have specifically explored the ways in which these variables interact to impact quality of life (QOL), particularly among patients with advanced cancer. The present study examined the role of optimism as a moderator of the relationship between social support and anxiety, depression, hopelessness, and QOL among patients with advanced cancer. METHODS: Participants (N = 168) completed self-report assessments of psychosocial, spiritual, and physical well-being, including social support, optimism, hopelessness, depressive and anxious symptoms, and QOL. Hierarchical multiple regression analyses were conducted to determine the extent to which social support and optimism were associated with depressive and anxious symptomatology, hopelessness and QOL, and the potential role of optimism as a moderator of the relationship between social support and these variables. RESULTS: Higher levels of optimism were significantly associated with fewer anxious and depressive symptoms, less hopelessness, and better QOL. Higher levels of perceived social support were also significantly associated with better QOL. Additionally, optimism moderated the relationship between social support and anxiety, such that there was a strong negative association between social support and anxiety for participants with low optimism. CONCLUSIONS: This study highlights the importance of optimism and social support in the QOL of patients with advanced cancer. As such, interventions that attend to patients' expectations for positive experiences and the expansion of social support should be the focus of future clinical and research endeavors.
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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 | 0.000 | 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) | 0.001 | 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".