Cancer, mental disorders, suicidal ideation and attempts in a large community sample
Bibliographic record
Abstract
PURPOSE: To determine the association between cancer diagnosis, mental disorders and suicidal behavior among community dwelling adults. METHODS: Data were drawn from the nationally representative Canadian Community Health Survey Cycle 1.2 (N=36 984, response rate 77%, age 15+). Respondents were grouped into three age groups (15-54, 55-74, and 75+ years), and multiple regression analyses were conducted to examine the relationship between cancer and mental disorders: unadjusted and adjusted for sociodemographics, social supports and other mental disorders. RESULTS: Among respondents aged 15-54, cancer was associated with increased odds of major depression (odds ratio [OR]=3.18; 95% confidence interval [CI]: 1.69-5.96), panic attacks (OR=2.15; 95% CI: 1.22-3.77) and any mental disorder. Among respondents aged 55-75, cancer was associated with increased odds of agoraphobia (OR=5.94; 95% CI: 1.68-21.03) and decreased odds of social phobia (OR=0.22; 95% CI: 0.06-0.80). Cancer was not associated with any mental disorder in the 75+ age group. Results persisted after adjustments for the covariates. Suicidal ideation was associated with cancer in the 55-74 age group (OR=5.07; 95% CI: 1.25-20.47) in unadjusted models; however, this relationship became non-significant when adjusting for the other covariates. CONCLUSION: Clinicians should consider screening for depression and panic disorder in young, community dwelling patients with cancer.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".