P2-84 Chronic conditions and major depression in community-dwelling older adults
Bibliographic record
Abstract
Objectives To estimate (1) the prevalence of long-term medical conditions and of comorbid major depression, and (2) the associations between major depression and various chronic medical conditions in a general population of older adults (over 50 years of age) and in persons who are traditionally classified as seniors (65 years and older). Methods Data from the Canadian Community Health Survey- Mental Health and Wellbeing (CCHS-1.2) were analysed. For the purposes of these analyses the dataset was restricted to those aged 50 and over (n=15 591). Chronic health conditions were assessed using a self-report method of doctor diagnosis. The World Mental Health-Composite Diagnostic Interview was used to assess major depressive episodes based on DSM-IV criteria. Results The overall prevalence of having at least one chronic condition in those over 50 years of age was 82.4%, compared to 62.0% in those under 50. The prevalence of a major depressive episode in those over 50 with one chronic condition was 3.7%, compared with 1.0% in those without a long-term medical condition. The top 3 chronic health conditions in seniors aged 65 or older were arthritis/rheumatism, high blood pressure and back problems. Chronic Fatigue Syndrome, fibromyalgia and migraine headache had the highest comorbidity with major depression in the senior population. Conclusions Differences were found between rates of chronic conditions and major depression between the general population, older adults and seniors in this study. Primary and secondary prevention efforts should target seniors who exhibit symptoms of depression or highly prevalent chronic health conditions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".