Depressive symptoms among cancer patients: Variation by gender, cancer type, and social engagement
Bibliographic record
Abstract
Prior literature has documented an association between cancer and depressive symptoms. There has been a limited understanding about whether the association between cancer and depressive symptoms varies by gender and whether social engagement moderates this association. Using seven waves of the Korean Longitudinal Study of Ageing (N = 10,055), we examine the association between cancer and depressive symptoms among middle- and older-aged adults in Korea. We conduct fixed-effects regression models to account for unobserved characteristics of individuals that may confound this association. We first investigate whether the association between cancer and depressive symptom differs by gender. We distinguish among cancer types to assess potentially distinctive mental health consequences of different types of cancer. Then, we explore whether social engagement moderates the cancer-depressive symptoms association. Naive OLS models yielded significant associations between cancer and depressive symptoms for both men and women. However, our preferred fixed effects estimates revealed that the association was statistically significant only for men, and not for women. This association was especially pronounced for lung cancer. We also found that one's level of social engagement including informal connections and formal social activities moderates the link between cancer and depressive symptoms. Cancer is not only a leading cause of death, but also a serious threat to one's mental health. This study sheds light on gender differences in psychological reactions to cancer among Korean adults. Findings of this study hold important implications for programs aiming to improve the mental health and quality of life of cancer patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".