Age-related patterns in negative affect and appraisals about colorectal cancer over time.
Bibliographic record
Abstract
OBJECTIVE: Age differences in reactions to cancer are established but poorly understood. The purpose of this study was to apply the theoretical model of strength and vulnerability integration (SAVI; Charles, 2010) to understand age-related patterns in emotional experiences and cancer appraisals among people diagnosed with colorectal cancer. METHODS: Individuals recently diagnosed with colorectal cancer (N = 139; 28-89 years-old) completed measures of positive and negative affect, depressive symptoms, and appraisals about cancer at four time points: baseline (prior to colorectal cancer surgery), 6-, 12-, and 18-months postsurgery. Multilevel modeling examined changes in affective experience and appraisals over time, across age, and the interaction of time by age. RESULTS: Negative affect decreased more rapidly over time among older adults than younger adults, p < .05, but positive affect was reasonably stable and unrelated to age. Depressive symptoms were also fairly stable over time, but consistently higher among younger adults, p < .01. Older age was significantly related to lower threat appraisals and greater levels of challenge, p < .01. Threat, but not challenge, mediated the age-by-time interaction predicting negative affect. CONCLUSIONS: Older age was related to lower levels of depressive symptoms and negative affect. Older adults also reported more adaptive appraisals of their cancer, which accounted for their more rapid decline in negative affect compared to younger adults. Overall, SAVI is a useful model for understanding age-related patterns in emotional well-being and appraisals in the context of colorectal cancer.
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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".