The relationship between mood disorder diagnosis and experiencing an unmet health-care need in Canada: findings from the 2014 Canadian Community Health Survey
Bibliographic record
Abstract
BACKGROUND: Despite Canada's universal health-care system, millions of Canadians experience unmet health-care needs (UHCN). People with mood disorders may be at higher risk of UHCN due to barriers such as stigma and gaps in health-care services. AIM: We aimed to examine the relationship between having a diagnosed mood disorder and experiencing UHCN using a recent, nationally representative survey. METHODS: Using the 2014 Canadian Community Health Survey, we used multivariate logistic regression to estimate the association between mood disorder and UHCN in the past 12 months, adjusting for sociodemographic variables and health status. RESULTS: Among 52,825 respondents, 11.8% reported UHCN. Respondents with a diagnosed mood disorder were more likely to report UHCN [adjusted odds ratio (OR) 1.61, 95% confidence interval (CI) 1.38, 1.89]. Among respondents with a regular doctor, people with mood disorders were still more likely to report UHCN (OR 1.63, 95% CI 1.38, 1.93). Sensitivity analyses using propensity score and missing data imputation approaches resulted in similar estimates. CONCLUSIONS: Adults diagnosed with a mood disorder are more likely to report UHCN in the past year, even those with a regular doctor. Our findings suggest that barriers beyond physician attachment may impact access to care for people with mood disorders.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| 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".