Personality Traits and Urinary Symptoms Are Associated with Mental Health Distress in Patients with a Diagnosis of Prostate Cancer
Bibliographic record
Abstract
OBJECTIVE: With a prolonged natural history compared with many other cancers, prostate cancer patients have high rates of mental illness over the duration of their treatment. Here, we examine the relationship between personality and mental health distress in a sample of prostate cancer patients. METHODS: This study was conducted in the Canadian Maritime provinces, where a cohort of 189 men with prostate cancer were invited to complete a quality-of-life online survey between May 2017 and December 2019. The presence or absence of screening positive for mental health illness was the primary outcome and was assessed using Kessler's 10-item scale (K10). Urinary symptoms were assessed using the International Prostate Symptom Score (IPSS). The ten-item personality inventory (TIPI) assessed extraversion, agreeableness, conscientiousness, emotional stability (or neuroticism), and openness to experiences. A multivariate logistic regression model was created to examine the association between personality, urinary symptoms, and mental health distress, while controlling for time from diagnosis, treatment type, age, and multimorbidity. RESULTS: Screening positive for mental illness (18.0%) was associated with personality traits of low levels of emotional stability (OR = 0.07, 95% CI: 0.03-0.20) and moderate to severe urinary problems (OR = 5.21, 95% CI: 1.94-14.05)). There was no identified association between treatment received for prostate cancer and personality type. CONCLUSION: Screening for mental health illness in this population may help reduce morbidity associated with cancer treatment, as well as identify patients who may be at risk of mental health distress and could benefit from individualized mental health support services. These findings suggest that multidisciplinary care is essential for the management of these patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".