Hidden Morbidity in Cancer: Spouse Caregivers
Bibliographic record
Abstract
PURPOSE: This study assesses psychological distress among advanced cancer patients and their spouse caregivers, while examining the relative contribution of caregiving burden and relational variables (attachment orientation and marital satisfaction) to depressive symptoms in the spouse caregivers. METHODS: A total of 101 patients with advanced GI or lung cancer and their spouse caregivers were recruited for the study. Measures included Beck Depression Inventory-II (BDI-II), Caregiving Burden scale, Experiences in Close Relationships scale, and ENRICH Marital Satisfaction scale. RESULTS: A total of 38.9% of the caregivers reported significant symptoms of depression (BDI-II > or = 15) compared with 23.0% of their ill spouses (P < .0001). In a hierarchical regression predicting caregiver's depression, spouse caregiver's age and patient's cancer site were entered in the first step, objective caregiving burden was entered in the second step, subjective caregiving burden was entered in the third step, caregiver's attachment scores were entered in the fourth step, and caregiver's marital satisfaction score was entered in the fifth step. The final model accounted for 37% of the variance of caregiver depression, with subjective caregiving burden (beta = .38; P < .01), caregiver's anxious attachment (beta = .21; P < .05), caregiver's avoidant attachment (beta = .20; P < .05), and caregiver's marital satisfaction (beta = -.18; P < .05) making significant contributions to the model. CONCLUSION: Spouse caregivers of patients with advanced cancer are a high-risk population for depression. Subjective caregiving burden and relational variables, such as caregivers' attachment orientations and marital dissatisfaction, are important predictors of caregiver depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".