A cross-sectional gender-sensitive analysis of depressive symptoms in patients with advanced cancer
Bibliographic record
Abstract
Background: Patients with advanced cancer commonly report depressive symptoms. Examinations of gender differences in depressive symptoms in patients with advanced cancer have yielded inconsistent findings. Aim: The objective of this study was to investigate whether the severity and correlates of depressive symptoms differ by gender in patients with advanced cancer. Design: Participants completed measures assessing sociodemographic and medical characteristics, disease burden, and psychosocial factors. Depressive symptoms were examined using the Patient Health Questionnaire, and other measures included physical functioning, symptom burden, general anxiety, death related distress, and dimensions of demoralization. A cross-sectional analysis examined the univariate and multivariate relationships between gender and depressive symptoms, while controlling for important covariates in multivariate analyses. Setting/participants: Patients with advanced cancer ( N = 305, 40% males and 60% females) were recruited for a psychotherapy trial from outpatient oncology clinics at a comprehensive cancer center in Canada. Results: Severity of depressive symptoms was similar for males ( M = 7.09, SD = 4.59) and females ( M = 7.66, SD = 5.01), t(303) = 1.01, p = 0.314. Greater general anxiety and number of cancer symptoms were associated with depressive symptoms in both males and females. Feeling like a failure ( β = 0.192), less death anxiety ( β = –0.188), severity of cancer symptoms ( β = 0.166), and older age ( β = 0.161) were associated with depressive symptoms only in males, while disheartenment ( β = 0.216) and worse physical functioning ( β = 0.275), were associated with depressive symptoms only in females. Conclusions: Males and females report similar levels of depressive symptoms but the pathways to depression may differ by gender. These differences suggest the potential for gender-based preventive and therapeutic interventions in this population.
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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 | 0.002 |
| 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".