Bibliographic record
Abstract
Background: Mental health concerns in late-life is a growing public health challenge as the population aged 65 and older rapidly increases locally and worldwide. An updated understanding of the causes of mood disorders in late-life and their consequences could guide interventions for this underrecognized and undertreated problem. We undertook a population-based analysis to quantify the prevalence of mood disorders in late-life in Ontario, Canada and to identify potential risk factors, and consequences.
 Methods: Individuals aged 65 or older participating in 4 cycles of a nationally-representative survey were included. A self-reported diagnosis of a mood disorder was used to classify individuals with mood disorders. Using linked administrative data, we quantified associations between potential risk factors, such as demographic/socioeconomic factors, substance use, and morbidity, and mood disorder. We also determined associations between mood disorders and outcomes (health service utilization and mortality) 5 years after the index interview date.
 Findings: The overall prevalence of mood disorders was 6.1% (4.9% among males,7.1% among females). The proportion of individuals with a mood disorder was higher among females for all potential risk factors. Statistically significant associations with mood disorder included age, sex, food insecurity, chronic opioid use, smoking, and morbidity. Individuals with mood disorders had increased odds of all long-term consequences, including hospitalization (adjusted OR [odds ratio]=1.55 95% CI [confidence interval]: 1.31-1.83); admission to long-term care (adjusted OR=2.28 95% CI: 1.71-3.02); and death (adjusted OR=1.35 95% CI: 1.13-1.63).
 Interpretation: Mood disorders in late-life were strongly correlated with demographic and social/behavioural factors as well as long-term health utilization outcomes. The understanding of correlations between potential risk factors for mood disorders in late-life provides a basis for potential interventions to reduce their occurrence and consequences. Interventions that target females, younger age groups, those with food insecurity or substance use, and individuals with co-morbidities may be promising.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".