Impact of arthritis on the perceived need and use of mental healthcare among Canadians with mental disorders: nationally representative cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between having arthritis and the perceived need for mental healthcare and use of mental health support among individuals with mental disorders. DESIGN: A cross-sectional analysis using data from Canadian Community Health Survey-Mental Health (2012). SETTING: The survey was administered across Canada's 10 provinces using multistage cluster sampling. PARTICIPANTS: The study sample consisted of individuals reporting depression, anxiety or bipolar disorder. STUDY VARIABLES AND ANALYSIS: The explanatory variable was self-reported doctor-diagnosed arthritis, and outcomes were perceived need for mental healthcare and use of mental health support. We computed overall and gender-stratified multivariable binomial logistic regression models adjusted for age, gender, race/ethnicity, income and geographical region. RESULTS: Among 1774 individuals with a mental disorder in the study sample, 436 (20.4%) reported having arthritis. Arthritis was associated with increased odds of having a perceived need for mental healthcare (adjusted OR (aOR) 1.71, 95% CI 1.06 to 2.77). In the gender-stratified models, this association was increased among men (aOR 2.69, 95% CI 1.32 to 5.49) but not women (aOR 1.48, 95% CI 0.78 to 2.82). Evaluation of the association between arthritis and use of mental health support resulted in an aOR of 1.50 (95% CI 0.89 to 2.51). Individuals with arthritis tended to use medications and professional services as opposed to non-professional support. CONCLUSION: Comorbid arthritis among individuals with a mental disorder was associated with an increased perceived need for mental healthcare, especially in men, underscoring the importance of understanding the role of masculinity in health seeking. Assessing the mental health of patients with arthritis continues to be essential for clinical care.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".