Health services use for mental health problems by community-living seniors with depression
Bibliographic record
Abstract
BACKGROUND: The literature on health services (HS) use for mental health problems by community-living seniors with depression is sparse. This study aimed to characterize patterns of HS use for mental health problems by seniors with depressive disorders and symptoms during the previous 12 months. METHOD: The study used data from the Canadian Community Health Survey Cycle 1.2: Mental Health and Well-Being (N = 7736). Information was collected on demographic, social, mental, physical and functional variables and HS use for mental health problems. We obtained simple univariate and bivariate descriptions of the data and developed multivariate predictive models for each of the HS utilization variables. RESULTS: Rates of any HS use for mental health problems ranged from 1.8% for those with no depressive symptoms to 31.1% for those with major depression. Variables predicting increased HS use were: depressive disorder or symptoms, clinically significant distress or impairment, age 65-84, single, post-secondary education, religiousness, disability, co-morbid mental disorder and fewer friends and positive social interactions. Variables predicting HS use among depressed seniors were physical health, psychiatric co-morbidity and activity limitation. CONCLUSION: Community-living seniors with major depression, co-morbid major depression and depressive symptoms were more likely to use psychiatrist, family physician, other professional and self-help services for mental health problems but less than a third actually did so.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".