Psychiatric Illness in Relation to Frailty in Community-Dwelling Elderly People without Dementia: A Report from the Canadian Study of Health and Aging
Bibliographic record
Abstract
We investigated whether frailty, defined as the accumulation of multiple, interacting illnesses, impairments and disabilities, is associated with psychiatric illness in older adults. Five-thousand-six-hundred-and-seventy-six community dwellers without dementia were identified within the Canadian Study of Health and Aging, and self-reported psychiatric illness was compared by levels of frailty (defined by an index of deficits that excluded mental illnesses). People with psychiatric illness (12.6% of those surveyed, who chiefly reported depression) had a higher mean frailty index value than those who did not. Older age was not associated with higher odds of psychiatric illness. Taking sex, frailty, and education into account, the odds of psychiatric illness decreased with each increasing year of age (OR 0.95; 95% CI, 0.94-0.97). Frailty was associated with psychiatric illness; for each additional deficit-defining frailty, odds of psychiatric illness increased (OR 1.23; 95% CI, 1.19-1.26). Similarly, psychiatric illness was associated with much higher odds of being among the most frail. These findings lend support to a multidimensional conceptualization of frailty. Our data also suggest that health care professionals who work with older adults with psychiatric illness should expect frailty to be common, and that those working with frail seniors should consider the possible co-existence of depression and psychiatric illness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".