Multimorbidity, disability, and mental health conditions in a nationally representative sample of middle-aged and older Canadians
Bibliographic record
Abstract
We aimed to estimate the prevalence of four mental health conditions as well as explore the association of multimorbidity, disability, and these problems among middle-aged and older Canadians. We used a subsample (N = 13,096) of the 2012 cross-sectional Canadian Community Health Survey-Mental Health Component. This data was used because it remains the most recent national survey that provided a comprehensive assessment of the major mental health conditions. Both ordinal and binary logistic regression models were fitted. Univariate and multivariate models assessed the association of multimorbidity, disability, and four mental health conditions. Descriptive statistics, prevalence estimates, and adjusted odds ratios, and 95% confidence intervals were reported. The prevalence of major depression, generalized anxiety disorder, suicide ideation, and poor self-rated mental health were 11.9%, 10.2%, 9.1%, and 7.7%, respectively. Multimorbidity and disability were significantly negatively associated with all the response variables except for disability and suicide ideation. We also found that (1) family history of mental health disorder, (2) personal history of mental health disorder, (3) stressful life experiences, and (4) low general life satisfaction negatively predicted all the conditions while higher household income and smoking status were protective factors. The cross-sectional nature of this study means that causality between predictor variables and outcomes cannot be inferred. Secondly, the use of self-reported data to derive the multimorbidity variable is subject to recall bias. This study highlights the need to create an integrated mental and physical healthcare support approach for middle-aged and older Canadians while taking into consideration age, sex, and racial differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.001 | 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".