Data_Sheet_1_Assessment of Current Mental Health Status in a Population-Based Sample of Canadian Men With and Without a History of Prostate Cancer Diagnosis: An Analysis of the Canadian Longitudinal Study on Aging (CLSA).docx
Bibliographic record
Abstract
Background: Small-scale studies indicate an increase in mental health disorders among prostate cancer survivors compared to the general population, but large population-based data assessing this relationship are scarce. The present study examined the prevalence of lifetime history of prostate cancer in a cross-sectional sample of Canadian men and assessed the contribution of lifetime history of a prostate cancer diagnosis, multimorbidity, and current alcohol and smoking status to the association with current mental health outcomes in this population. Methods: The analytical sample included 25,183 men (aged 45 to 85 years old), who completed a survey as part of the Canadian Longitudinal Study on Aging (CLSA). The Center for Epidemiological Studies Depression Scale (CES-D10), Kessler's Psychological Distress Scale (K10), and self-reported mental health were mental health outcomes. Multiple logistic regression analyses, and controlling for the complexity of the design and covariates, evaluated the association between prostate cancer survivorship, multimorbidity, alcohol and smoking status, and current mental health outcomes. Results: The prevalence of lifetime history of prostate cancer diagnosis in this population-based sample of men was 4% (95% CI: 3.7, 4.4). Our results indicate statistically significantly higher odds of current psychological distress (aOR = 1.52, 95% CI: 1.09, 2.11) and screening positive for depression (aOR = 1.24; 95% CI: 1.02, 1.51) among survivors of prostate cancer, compared to men without a history of prostate cancer diagnosis in demographics controlled analyses. After addition of multimorbidity and substance use, the odds of screening positive for depression among survivors of prostate cancer are 1.32 (95% CI: 1.06, 1.64) higher compared to men who never had a history of prostate cancer diagnosis. Interpretation: Patient education and empowerment programs aimed at addressing concerns during the diagnosis and treatment and enhancing survivorship care plans by adding routine screening for mental distress to help survivors overcome poor mental health during the cancer survivorship journey, are warranted.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.168 | 0.018 |
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".