Burden of depression among Canadian adults with cancer; results from a national survey
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence and association of depression among Canadian adults with cancer in a population-based context. METHODS: The Canadian Community Health Survey (CCHS) (2015-2016) was accessed and adult participants with cancer who completed the Personal Health Questionnaire (PHQ9) were included in the current analysis. Multivariable logistic regression was conducted to elucidate the factors associated with the development of depression. An additional multivariable logistic regression analysis was conducted to evaluate the association of depression with ever contemplating suicide (suicidal ideation). RESULTS: A total of 867 participants with cancer have completed PHQ9 were included in the current analysis (including 603 participants (69.6%) without depression (PHQ9 ≤ 4) and 264 participants (30.4%) with depression (PHQ9 > 4)). Moreover, 92 participants (10.6%) fulfill the criteria for moderate/severe depression (PHQ9 > 9). The following factors were associated with the presence of depression (PHQ9 > 4), female sex (OR for males versus females: 0.56; 95% CI: 0.34-0.93; P = 0.02); poor self-perceived health (OR for excellent health versus poor health: 0.12; 95% CI: 0.02-0.62; P = 0.01) and poor self-perceived mental health (OR for excellent mental health versus poor mental health: 0.02; 95% CI: <0.01-0.24; P < 0.01). Additional multivariable logistic regression analysis showed that depression (PHQ9 > 4) was associated with a higher probability of suicidal ideation (OR for no depression versus depression: 0.43; 95% CI: 0.21-0.91; P = 0.02). CONCLUSIONS: Depression seems to be an underdiagnosed and possibly undertreated comorbid condition among Canadian adults 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".