Severe disability and self-reported depression and anxiety among persons living with Type 2 diabetes in Canada
Bibliographic record
Abstract
This study aims to estimate the national prevalence of self-reported depression and anxiety disorders among persons living with Type 2 diabetes in Canada, as well as to investigate the potential link between disability severity and these conditions. We conducted a cross-sectional analysis of a subsample (N=8,321) of participants living with Type 2 diabetes in the 2017-2018 Canadian Community Health Survey-Annual Component. Modified Poisson regression models were used to examine the association between disability severity and depression and anxiety disorders. Descriptive statistics, prevalence estimates, adjusted relative risk, and 95% confidence intervals are reported. All statistical analyses were conducted using STATA version 18. The prevalence of depression and anxiety were 12.4% and 10.1% respectively. Our study found disability severity as a strong risk factor for both psychiatric disorders. We also found (1) dissatisfaction with life, (2) extremely stressful life events, (3) being female (4) single or never married, (5) poor self-rated health, (6) obesity, and (7) current smoker as significant risk factors for both psychiatric disorders. Conversely, increasing age and higher annual personal income status were significant protective factors. The study's limitations include the inability to establish temporal connections between risk factors and psychiatric disorders due to the cross-sectional design and the exclusion of those living in remote parts of the country and others from participation in the survey, which may underestimate the prevalence of Type 2 diabetes and severe disability. This study's findings point toward a growing demand for tertiary prevention to increase the probability for those living with Type 2 diabetes in Canada of maintaining functional health, improving mental health, and having a better quality of life. Public health prevention efforts targeted at decreasing the prevalence of diabetes and its complications and disability severity are recommended.
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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 | 0.001 |
| 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) | 0.001 | 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".