Examining the Pathoplastic Moderating Role of Education on the Association between Depressive Mood and Self-Rated Health among Cancer Survivors: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Self-rated health (SRH) is a salient patient outcome for cancer survivors, and depressive mood and education are known determinants of cancer survivors' SRH. Moving beyond the well-established direct association between depressive mood, education, and SRH among cancer survivors, this epidemiological study investigated the pathoplastic role of education on depressive mood in relation to SRH among a nationally representative sample of cancer survivors in the United States. METHODS: = 3844). Ordered logistic regression was used to evaluate the direct impact of depressive mood and education in relation to SRH. In addition, the pathoplastic moderating effect was evaluated using ordered logistic regression with an interaction term of depressive mood and education in the regression model. All analyses adjusted for complex sample weights so that findings are nationally representative. RESULTS: After adjusting for all covariates, U.S. cancer survivors' depressive mood was significantly associated with lower SRH, and U.S. cancer survivors' higher education was significantly associated with higher SRH. As a pathoplastic moderator, cancer survivors' education significantly moderated the association between depressive mood and SRH. The negative association between depressive mood and SRH was significantly greater among those with higher education. CONCLUSION: Moving beyond the direct association between depressive mood, education, and SRH, education served as a pathoplastic moderator in relation to depressive mood and SRH. Psycho-oncology providers need to be mindful of the "protective-risk" effect of education in relation to cancer survivors' depressive mood and SRH.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".